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Ren Z, Lan Q, Zhang Y, Wang S. Exploring simple triplet representation learning. Comput Struct Biotechnol J 2024; 23:1510-1521. [PMID: 38633386 PMCID: PMC11021836 DOI: 10.1016/j.csbj.2024.04.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2024] [Revised: 04/01/2024] [Accepted: 04/01/2024] [Indexed: 04/19/2024] Open
Abstract
Fully supervised learning methods necessitate a substantial volume of labelled training instances, a process that is typically both labour-intensive and costly. In the realm of medical image analysis, this issue is further amplified, as annotated medical images are considerably more scarce than their unlabelled counterparts. Consequently, leveraging unlabelled images to extract meaningful underlying knowledge presents a formidable challenge in medical image analysis. This paper introduces a simple triple-view unsupervised representation learning model (SimTrip) combined with a triple-view architecture and loss function, aiming to learn meaningful inherent knowledge efficiently from unlabelled data with small batch size. With the meaningful representation extracted from unlabelled data, our model demonstrates exemplary performance across two medical image datasets. It achieves this using only partial labels and outperforms other state-of-the-art methods. The method we present herein offers a novel paradigm for unsupervised representation learning, establishing a baseline that is poised to inspire the development of more intricate SimTrip-based methods across a spectrum of computer vision applications. Code and user guide are released at https://github.com/JerryRollingUp/SimTripSystem, the system also runs at http://43.131.9.159:5000/.
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Affiliation(s)
- Zeyu Ren
- University of Leicester, Leicester, UK
| | - Quan Lan
- Department of Neurology, First Affiliated Hospital of Xiamen University, China
| | - Yudong Zhang
- University of Leicester, Leicester, UK
- Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Shuihua Wang
- University of Leicester, Leicester, UK
- Department of Biological Sciences, Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu 215123, China
- Department of Mathematical Sciences, University of Liverpool, Liverpool, L69 3BX, UK
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2
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Kumar A, Vishwakarma A, Bajaj V. ML3CNet: Non-local means-assisted automatic framework for lung cancer subtypes classification using histopathological images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 251:108207. [PMID: 38723437 DOI: 10.1016/j.cmpb.2024.108207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 03/20/2024] [Accepted: 04/30/2024] [Indexed: 05/31/2024]
Abstract
BACKGROUND AND OBJECTIVE Lung cancer (LC) has a high fatality rate that continuously affects human lives all over the world. Early detection of LC prolongs human life and helps to prevent the disease. Histopathological inspection is a common method to diagnose LC. Visual inspection of histopathological diagnosis necessitates more inspection time, and the decision depends on the subjective perception of clinicians. Usually, machine learning techniques mostly depend on traditional feature extraction which is labor-intensive and may not be appropriate for enormous data. In this work, a convolutional neural network (CNN)-based architecture is proposed for the more effective classification of lung tissue subtypes using histopathological images. METHODS Authors have utilized the first-time nonlocal mean (NLM) filter to suppress the effect of noise from histopathological images. NLM filter efficiently eliminated noise while preserving the edges of images. Then, the obtained denoised images are given as input to the proposed multi-headed lung cancer classification convolutional neural network (ML3CNet). Furthermore, the model quantization technique is utilized to reduce the size of the proposed model for the storage of the data. Reduction in model size requires less memory and speeds up data processing. RESULTS The effectiveness of the proposed model is compared with the other existing state-of-the-art methods. The proposed ML3CNet achieved an average classification accuracy of 99.72%, sensitivity of 99.66%, precision of 99.64%, specificity of 99.84%, F-1 score of 0.9965, and area under the curve of 0.9978. The quantized accuracy of 98.92% is attained by the proposed model. To validate the applicability of the proposed ML3CNet, it has also been tested on the colon cancer dataset. CONCLUSION The findings reveal that the proposed approach can be beneficial to automatically classify LC subtypes that might assist healthcare workers in making decisions more precisely. The proposed model can be implemented on the hardware using Raspberry Pi for practical realization.
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Affiliation(s)
- Anurodh Kumar
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India.
| | - Amit Vishwakarma
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India.
| | - Varun Bajaj
- PDPM Indian Institute of Information Technology, Design and Manufacturing, Jabalpur, 482005, India; Maulana Azad National Institute of Technology Bhopal, Bhopal, 462003, India.
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Jabeen I, Altemimi AB, Rabail R, Kafeel S, Shahid A, Inam-Ur-Raheem M, Mousavi Khaneghah A, Aadil RM. Exploring the astonishing beneficial effects of round gourd (Praecitrullus fistulosus) and plant lectins towards cancer: A comprehensive review. Int J Biol Macromol 2024; 271:132629. [PMID: 38815952 DOI: 10.1016/j.ijbiomac.2024.132629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2024] [Revised: 05/20/2024] [Accepted: 05/22/2024] [Indexed: 06/01/2024]
Abstract
Praecitrullus fistulosus, commonly known as round gourd or tinda, is a remarkable source of bioactive substances like polyphenols, antioxidants, carotene, magnesium, and vitamin C. It is considered one of the Cucurbitaceae family due to its medicinal features. Plant lectins are carbohydrate-binding proteins that can bind and identify the carbohydrate moieties upon cancerous cells demonstrated some anticancer potentials. Several plant lectins are helpful as cancer biomarkers because they can find cancer cells and contribute to cell death initiation via apoptosis and autophagy, suggesting the possible role of cancer-inhibiting pathways. Therefore, round gourd and lectins might be useful in the controlling of cancer. This study compiled the most recent scientific literature regarding the round gourd and numerous plant lectins, and the clinical trials of lectins exploring their effects on cancer were examined. Research according to the literature, round gourd, and lectins demonstrated pharmacological alterations not only in cancer but in many other disorders as well. Thus, clinical investigations proved the beneficial impacts of round gourd and lectins on cancer due to their antioxidants, anti-inflammatory, and anticarcinogenic properties. Further studies are required to fully comprehend the potential applications of these plant-derived compounds against cancer, as well as to identify the round gourd components and clarify their mode of action.
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Affiliation(s)
- Ifrah Jabeen
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
| | - Ammar B Altemimi
- Food Science Department, College of Agriculture, University of Basrah, Basrah 61004, Iraq; College of Medicine, University of Warith Al-Anbiyaa, Karbala 56001, Iraq
| | - Roshina Rabail
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
| | - Sadia Kafeel
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
| | - Arashi Shahid
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan
| | - Muhammad Inam-Ur-Raheem
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan.
| | - Amin Mousavi Khaneghah
- Faculty of Biotechnologies (BioTech), ITMO University 191002, 9 Lomonosova Street, Saint Petersburg, Russia; Halal Research Center of IRI, Iran Food and Drug Administration, Ministry of Health and Medical Education, Tehran, Iran.
| | - Rana Muhammad Aadil
- National Institute of Food Science and Technology, University of Agriculture, Faisalabad 38000, Pakistan.
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Musthafa MM, Manimozhi I, Mahesh TR, Guluwadi S. Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques. BMC Med Inform Decis Mak 2024; 24:142. [PMID: 38802836 PMCID: PMC11131269 DOI: 10.1186/s12911-024-02553-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 05/22/2024] [Indexed: 05/29/2024] Open
Abstract
Lung cancer remains a leading cause of cancer-related mortality globally, with prognosis significantly dependent on early-stage detection. Traditional diagnostic methods, though effective, often face challenges regarding accuracy, early detection, and scalability, being invasive, time-consuming, and prone to ambiguous interpretations. This study proposes an advanced machine learning model designed to enhance lung cancer stage classification using CT scan images, aiming to overcome these limitations by offering a faster, non-invasive, and reliable diagnostic tool. Utilizing the IQ-OTHNCCD lung cancer dataset, comprising CT scans from various stages of lung cancer and healthy individuals, we performed extensive preprocessing including resizing, normalization, and Gaussian blurring. A Convolutional Neural Network (CNN) was then trained on this preprocessed data, and class imbalance was addressed using Synthetic Minority Over-sampling Technique (SMOTE). The model's performance was evaluated through metrics such as accuracy, precision, recall, F1-score, and ROC curve analysis. The results demonstrated a classification accuracy of 99.64%, with precision, recall, and F1-score values exceeding 98% across all categories. SMOTE significantly enhanced the model's ability to classify underrepresented classes, contributing to the robustness of the diagnostic tool. These findings underscore the potential of machine learning in transforming lung cancer diagnostics, providing high accuracy in stage classification, which could facilitate early detection and tailored treatment strategies, ultimately improving patient outcomes.
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Affiliation(s)
| | - I Manimozhi
- Department of Computer science and Engineering, East Point College of Engineering & Technology, Bangalore, India
| | - T R Mahesh
- Department of Computer Science and Engineering, JAIN (Deemed-to-be University), Bengaluru, 562112, India
| | - Suresh Guluwadi
- Adama Science and Technology University, Adama, 302120, Ethiopia.
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5
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Uddin AH, Chen YL, Akter MR, Ku CS, Yang J, Por LY. Colon and lung cancer classification from multi-modal images using resilient and efficient neural network architectures. Heliyon 2024; 10:e30625. [PMID: 38742084 PMCID: PMC11089372 DOI: 10.1016/j.heliyon.2024.e30625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2024] [Revised: 04/02/2024] [Accepted: 04/30/2024] [Indexed: 05/16/2024] Open
Abstract
Automatic classification of colon and lung cancer images is crucial for early detection and accurate diagnostics. However, there is room for improvement to enhance accuracy, ensuring better diagnostic precision. This study introduces two novel dense architectures (D1 and D2) and emphasizes their effectiveness in classifying colon and lung cancer from diverse images. It also highlights their resilience, efficiency, and superior performance across multiple datasets. These architectures were tested on various types of datasets, including NCT-CRC-HE-100K (set of 100,000 non-overlapping image patches from hematoxylin and eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue), CRC-VAL-HE-7K (set of 7180 image patches from N = 50 patients with colorectal adenocarcinoma, no overlap with patients in NCT-CRC-HE-100K), LC25000 (Lung and Colon Cancer Histopathological Image), and IQ-OTHNCCD (Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases), showcasing their effectiveness in classifying colon and lung cancers from histopathological and Computed Tomography (CT) scan images. This underscores the multi-modal image classification capability of the proposed models. Moreover, the study addresses imbalanced datasets, particularly in CRC-VAL-HE-7K and IQ-OTHNCCD, with a specific focus on model resilience and robustness. To assess overall performance, the study conducted experiments in different scenarios. The D1 model achieved an impressive 99.80 % accuracy on the NCT-CRC-HE-100K dataset, with a Jaccard Index (J) of 0.8371, a Matthew's Correlation Coefficient (MCC) of 0.9073, a Cohen's Kappa (Kp) of 0.9057, and a Critical Success Index (CSI) of 0.8213. When subjected to 10-fold cross-validation on LC25000, the D1 model averaged (avg) 99.96 % accuracy (avg J, MCC, Kp, and CSI of 0.9993, 0.9987, 0.9853, and 0.9990), surpassing recent reported performances. Furthermore, the ensemble of D1 and D2 reached 93 % accuracy (J, MCC, Kp, and CSI of 0.7556, 0.8839, 0.8796, and 0.7140) on the IQ-OTHNCCD dataset, exceeding recent benchmarks and aligning with other reported results. Efficiency evaluations were conducted in various scenarios. For instance, training on only 10 % of LC25000 resulted in high accuracy rates of 99.19 % (J, MCC, Kp, and CSI of 0.9840, 0.9898, 0.9898, and 0.9837) (D1) and 99.30 % (J, MCC, Kp, and CSI of 0.9863, 0.9913, 0.9913, and 0.9861) (D2). In NCT-CRC-HE-100K, D2 achieved an impressive 99.53 % accuracy (J, MCC, Kp, and CSI of 0.9906, 0.9946, 0.9946, and 0.9906) with training on only 30 % of the dataset and testing on the remaining 70 %. When tested on CRC-VAL-HE-7K, D1 and D2 achieved 95 % accuracy (J, MCC, Kp, and CSI of 0.8845, 0.9455, 0.9452, and 0.8745) and 96 % accuracy (J, MCC, Kp, and CSI of 0.8926, 0.9504, 0.9503, and 0.8798), respectively, outperforming previously reported results and aligning closely with others. Lastly, training D2 on just 10 % of NCT-CRC-HE-100K and testing on CRC-VAL-HE-7K resulted in significant outperformance of InceptionV3, Xception, and DenseNet201 benchmarks, achieving an accuracy rate of 82.98 % (J, MCC, Kp, and CSI of 0.7227, 0.8095, 0.8081, and 0.6671). Finally, using explainable AI algorithms such as Grad-CAM, Grad-CAM++, Score-CAM, and Faster Score-CAM, along with their emphasized versions, we visualized the features from the last layer of DenseNet201 for histopathological as well as CT-scan image samples. The proposed dense models, with their multi-modality, robustness, and efficiency in cancer image classification, hold the promise of significant advancements in medical diagnostics. They have the potential to revolutionize early cancer detection and improve healthcare accessibility worldwide.
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Affiliation(s)
- A. Hasib Uddin
- Department of Computer Science and Engineering, Khwaja Yunus Ali University, Enayetpur, Chouhali, Sirajganj, 6751, Bangladesh
| | - Yen-Lin Chen
- Department of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, 106344, Taiwan
| | - Miss Rokeya Akter
- Department of Computer Science and Engineering, Khwaja Yunus Ali University, Enayetpur, Chouhali, Sirajganj, 6751, Bangladesh
| | - Chin Soon Ku
- Department of Computer Science, Universiti Tunku Abdul Rahman, Kampar, 31900, Malaysia
| | - Jing Yang
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
| | - Lip Yee Por
- Department of Computer System and Technology, Faculty of Computer Science and Information Technology, Universiti Malaya, 50603, Kuala Lumpur, Malaysia
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Lu W, Wang Q, Liu L, Luo W. Exploring the mystery of colon cancer from the perspective of molecular subtypes and treatment. Sci Rep 2024; 14:10883. [PMID: 38740818 DOI: 10.1038/s41598-024-60495-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 04/23/2024] [Indexed: 05/16/2024] Open
Abstract
The molecular categorization of colon cancer patients remains elusive. Gene set enrichment analysis (GSEA), which investigates the dysregulated genes among tumor and normal samples, has revealed the pivotal role of epithelial-to-mesenchymal transition (EMT) in colon cancer pathogenesis. In this study, we employed multi-clustering method for grouping data, resulting in the identification of two clusters characterized by varying prognostic outcomes. These two subgroups not only displayed disparities in overall survival (OS) but also manifested variations in clinical variables, genetic mutation, and gene expression profiles. Using the nearest template prediction (NTP) method, we were able to replicate the molecular classification effectively within the original dataset and validated it across multiple independent datasets, underscoring its robust repeatability. Furthermore, we constructed two prognostic signatures tailored to each of these subgroups. Our molecular classification, centered on EMT, hold promise in offering fresh insights into the therapy strategies and prognosis assessment for colon cancer.
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Affiliation(s)
- Wenhong Lu
- The Second Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, 410005, Hunan, People's Republic of China
| | - Qiwei Wang
- Hunan Provincial Rehabilitation Hospital, Changsha, 410007, Hunan, People's Republic of China
| | - Lifang Liu
- The First Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, 410007, Hunan, People's Republic of China
| | - Wenpeng Luo
- The Second Affiliated Hospital of Hunan University of Chinese Medicine, Changsha, 410005, Hunan, People's Republic of China.
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7
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Golbabaei MH, Varnoosfaderani MS, Hemmati F, Barati MR, Pishbin F, Seyyed Ebrahimi SA. Machine learning-guided morphological property prediction of 2D electrospun scaffolds: the effect of polymer chemical composition and processing parameters. RSC Adv 2024; 14:15178-15199. [PMID: 38737974 PMCID: PMC11082644 DOI: 10.1039/d4ra01257g] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2024] [Accepted: 04/27/2024] [Indexed: 05/14/2024] Open
Abstract
Among various methods for fabricating polymeric tissue engineering scaffolds, electrospinning stands out as a relatively simple technique widely utilized in research. Numerous studies have delved into understanding how electrospinning processing parameters and specific polymeric solutions affect the physical features of the resulting scaffolds. However, owing to the complexity of these interactions, no definitive approaches have emerged. This study introduces the use of Simplified Molecular Input Line Entry System (SMILES) encoding method to represent materials, coupled with machine learning algorithms, to model the relationships between material properties, electrospinning parameters and scaffolds' physical properties. Here, the scaffolds' fiber diameter and conductivity have been predicted for the first time using this approach. In the classification task, the voting classifier predicted the fibers diameter with a balanced accuracy score of 0.9478. In the regression task, a neural network regressor was architected to learn the relations between parameters and predict the fibers diameter with R2 = 0.723. In the case of fibers conductivity, regressor and classifier models were used for prediction, but the performance fluctuated due to the inadequate information in the published data and the collected dataset. Finally, the model prediction accuracy was validated by experimental electrospinning of a biocompatible polymer (i.e., polyvinyl alcohol and polyvinyl alcohol/polypyrrole). Field-emission scanning electron microscope (FE-SEM) images were used to measure fiber diameter. These results demonstrated the efficacy of the proposed model in predicting the polymer nanofiber diameter and reducing the parameter space prior to the scoping exercises. This data-driven model can be readily extended to the electrospinning of various biopolymers.
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Affiliation(s)
- Mohammad Hossein Golbabaei
- School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran Tehran Iran +98 21 88006076 +98 21 61114065
| | | | - Farshid Hemmati
- School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran Tehran Iran +98 21 88006076 +98 21 61114065
| | - Mohammad Reza Barati
- School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran Tehran Iran +98 21 88006076 +98 21 61114065
| | - Fatemehsadat Pishbin
- School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran Tehran Iran +98 21 88006076 +98 21 61114065
| | - Seyyed Ali Seyyed Ebrahimi
- Advanced Magnetic Materials Research Center, School of Metallurgy and Materials Engineering, College of Engineering, University of Tehran Tehran Iran +98 21 88006076 +98 21 88225374
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8
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Vishwakarma M, Agrawal P, Soni S, Tomar S, Haider T, Kashaw SK, Soni V. Cationic nanocarriers: A potential approach for targeting negatively charged cancer cell. Adv Colloid Interface Sci 2024; 327:103160. [PMID: 38663154 DOI: 10.1016/j.cis.2024.103160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Revised: 04/04/2024] [Accepted: 04/16/2024] [Indexed: 05/07/2024]
Abstract
Cancer, a widespread and lethal disease, necessitates precise therapeutic interventions to mitigate its devastating impact. While conventional chemotherapy remains a cornerstone of cancer treatment, its lack of specificity towards cancer cells results in collateral damage to healthy tissues, leading to adverse effects. Thus, the quest for targeted strategies has emerged as a critical focus in cancer research. This review explores the development of innovative targeting methods utilizing novel drug delivery systems tailored to recognize and effectively engage cancer cells. Cancer cells exhibit morphological and metabolic traits, including irregular morphology, unchecked proliferation, metabolic shifts, genetic instability, and a higher negative charge, which serve as effective targeting cues. Central to these strategies is the exploitation of the unique negative charge characteristic of cancer cells, attributed to alterations in phospholipid composition and the Warburg effect. Leveraging this distinct feature, researchers have devised cationic carrier systems capable of enhancing the specificity of therapeutic agents towards cancer cells. The review delineates the underlying causes of the negative charge in cancer cells and elucidates various targeting approaches employing cationic compounds for drug delivery systems. Furthermore, it delves into the methods employed for the preparation of these systems. Beyond cancer treatment, the review also underscores the multifaceted applications of cationic carrier systems, encompassing protein and peptide delivery, imaging, photodynamic therapy, gene delivery, and antimicrobial applications. This comprehensive exploration underscores the potential of cationic carrier systems as versatile tools in the fight against cancer and beyond.
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Affiliation(s)
- Monika Vishwakarma
- Department of Pharmaceutical Sciences, Dr. Harisingh Gour Vishwavidyalaya, Sagar, MP, India
| | - Poornima Agrawal
- Department of Pharmaceutical Sciences, Dr. Harisingh Gour Vishwavidyalaya, Sagar, MP, India
| | - Sakshi Soni
- Department of Pharmaceutical Sciences, Dr. Harisingh Gour Vishwavidyalaya, Sagar, MP, India
| | - Surbhi Tomar
- Department of Pharmaceutical Sciences, Dr. Harisingh Gour Vishwavidyalaya, Sagar, MP, India
| | - Tanweer Haider
- Department of Pharmaceutical Sciences, Dr. Harisingh Gour Vishwavidyalaya, Sagar, MP, India; Amity Institute of Pharmacy, Amity University Madhya Pradesh, Gwalior 474005, MP, India
| | - Sushil K Kashaw
- Department of Pharmaceutical Sciences, Dr. Harisingh Gour Vishwavidyalaya, Sagar, MP, India
| | - Vandana Soni
- Department of Pharmaceutical Sciences, Dr. Harisingh Gour Vishwavidyalaya, Sagar, MP, India.
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Alsubai S. Transfer learning based approach for lung and colon cancer detection using local binary pattern features and explainable artificial intelligence (AI) techniques. PeerJ Comput Sci 2024; 10:e1996. [PMID: 38660170 PMCID: PMC11042027 DOI: 10.7717/peerj-cs.1996] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 03/27/2024] [Indexed: 04/26/2024]
Abstract
Cancer, a life-threatening disorder caused by genetic abnormalities and metabolic irregularities, is a substantial health danger, with lung and colon cancer being major contributors to death. Histopathological identification is critical in directing effective treatment regimens for these cancers. The earlier these disorders are identified, the lesser the risk of death. The use of machine learning and deep learning approaches has the potential to speed up cancer diagnosis processes by allowing researchers to analyse large patient databases quickly and affordably. This study introduces the Inception-ResNetV2 model with strategically incorporated local binary patterns (LBP) features to improve diagnostic accuracy for lung and colon cancer identification. The model is trained on histopathological images, and the integration of deep learning and texture-based features has demonstrated its exceptional performance with 99.98% accuracy. Importantly, the study employs explainable artificial intelligence (AI) through SHapley Additive exPlanations (SHAP) to unravel the complex inner workings of deep learning models, providing transparency in decision-making processes. This study highlights the potential to revolutionize cancer diagnosis in an era of more accurate and reliable medical assessments.
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Affiliation(s)
- Shtwai Alsubai
- Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
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10
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Sharkas M, Attallah O. Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform. Sci Rep 2024; 14:6914. [PMID: 38519513 PMCID: PMC10959971 DOI: 10.1038/s41598-024-56820-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 03/11/2024] [Indexed: 03/25/2024] Open
Abstract
Colorectal cancer (CRC) exhibits a significant death rate that consistently impacts human lives worldwide. Histopathological examination is the standard method for CRC diagnosis. However, it is complicated, time-consuming, and subjective. Computer-aided diagnostic (CAD) systems using digital pathology can help pathologists diagnose CRC faster and more accurately than manual histopathology examinations. Deep learning algorithms especially convolutional neural networks (CNNs) are advocated for diagnosis of CRC. Nevertheless, most previous CAD systems obtained features from one CNN, these features are of huge dimension. Also, they relied on spatial information only to achieve classification. In this paper, a CAD system is proposed called "Color-CADx" for CRC recognition. Different CNNs namely ResNet50, DenseNet201, and AlexNet are used for end-to-end classification at different training-testing ratios. Moreover, features are extracted from these CNNs and reduced using discrete cosine transform (DCT). DCT is also utilized to acquire spectral representation. Afterward, it is used to further select a reduced set of deep features. Furthermore, DCT coefficients obtained in the previous step are concatenated and the analysis of variance (ANOVA) feature selection approach is applied to choose significant features. Finally, machine learning classifiers are employed for CRC classification. Two publicly available datasets were investigated which are the NCT-CRC-HE-100 K dataset and the Kather_texture_2016_image_tiles dataset. The highest achieved accuracy reached 99.3% for the NCT-CRC-HE-100 K dataset and 96.8% for the Kather_texture_2016_image_tiles dataset. DCT and ANOVA have successfully lowered feature dimensionality thus reducing complexity. Color-CADx has demonstrated efficacy in terms of accuracy, as its performance surpasses that of the most recent advancements.
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Affiliation(s)
- Maha Sharkas
- Electronics and Communications Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, Egypt
| | - Omneya Attallah
- Electronics and Communications Engineering Department, College of Engineering and Technology, Arab Academy for Science, Technology, and Maritime Transport, Alexandria, Egypt.
- Wearables, Biosensing, and Biosignal Processing Laboratory, Arab Academy for Science, Technology and Maritime Transport, Alexandria, 21937, Egypt.
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11
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Chhillar I, Singh A. A feature engineering-based machine learning technique to detect and classify lung and colon cancer from histopathological images. Med Biol Eng Comput 2024; 62:913-924. [PMID: 38091162 DOI: 10.1007/s11517-023-02984-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 11/29/2023] [Indexed: 02/22/2024]
Abstract
Globally, lung and colon cancers are among the most prevalent and lethal tumors. Early cancer identification is essential to increase the likelihood of survival. Histopathological images are considered an appropriate tool for diagnosing cancer, which is tedious and error-prone if done manually. Recently, machine learning methods based on feature engineering have gained prominence in automatic histopathological image classification. Furthermore, these methods are more interpretable than deep learning, which operates in a "black box" manner. In the medical profession, the interpretability of a technique is critical to gaining the trust of end users to adopt it. In view of the above, this work aims to create an accurate and interpretable machine-learning technique for the automated classification of lung and colon cancers from histopathology images. In the proposed approach, following the preprocessing steps, texture and color features are retrieved by utilizing the Haralick and Color histogram feature extraction algorithms, respectively. The obtained features are concatenated to form a single feature set. The three feature sets (texture, color, and combined features) are passed into the Light Gradient Boosting Machine (LightGBM) classifier for classification. And their performance is evaluated on the LC25000 dataset using hold-out and stratified 10-fold cross-validation (Stratified 10-FCV) techniques. With a test/hold-out set, the LightGBM with texture, color, and combined features classifies the lung and colon cancer images with 97.72%, 99.92%, and 100% accuracy respectively. In addition, a stratified 10-fold cross-validation method also revealed that LightGBM's combined or color features performed well, with an excellent mean auc_mu score and a low mean multi_logloss value. Thus, this proposed technique can help histologists detect and classify lung and colon histopathology images more efficiently, effectively, and economically, resulting in more productivity.
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Affiliation(s)
- Indu Chhillar
- Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Haryana, India.
| | - Ajmer Singh
- Department of Computer Science and Engineering, Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Haryana, India
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Kadirappa R, S D, R P, Ko SB. DeepHistoNet: A robust deep-learning model for the classification of hepatocellular, lung, and colon carcinoma. Microsc Res Tech 2024; 87:229-256. [PMID: 37750465 DOI: 10.1002/jemt.24426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 08/24/2023] [Accepted: 09/12/2023] [Indexed: 09/27/2023]
Abstract
In recent days, non-communicable diseases (NCDs) require more attention since they require specialized infrastructure for treatment. As per the cancer population registry estimate, nearly 800,000 new cancer cases will be detected yearly. The statistics alarm the need for early cancer detection and diagnosis. Cancer identification can be made either through manual efforts or by computer-aided algorithms. Manual efforts-based cancer detection is labor intensive and also offers more time complexity. In contrast, computer-aided algorithms offer feasibility in reducing time and manual efforts. With the motivation to develop a computer-aided diagnosis system for NCD, we developed a cancer detection methodology. In the present article, a deep learning (DL)-based cancer identification model is developed. In DL-based architectures, the features are generally extracted using convolutional neural networks. The proposed attention-guided, densely connected residual, and dilated convolution deep neural network called DeepHistoNet acquire precise patterns for classification. Experimentation has been carried out on Kasturba Medical College (KMC), TCGA-LIHC, and LC25000 datasets to prove the robustness of the model. Performance evaluation metrics like F1-score, sensitivity, specificity, recall, and accuracy validate the experimentation. Experimental results demonstrate that the proposed DeepHistoNet model outperforms the other state-of-the-art methods. The proposed model has been able to classify the KMC liver dataset with 97.1% accuracy and 0.9867 value of area under the curve-receiver operating characteristic curve (AUC-ROC), which is the best result obtained compared to the state-of-the-art techniques. The performance of the DeepHistoNet has been even better on the LC25000 dataset. On the LC25000 dataset, the proposed model achieved 99.8% classification accuracy. To our knowledge, DeepHistoNet is a novel approach for multiple histopathological image classification. RESEARCH HIGHLIGHTS: A novel robust DL model is proposed for histopathological image carcinoma classification. The precise patterns for accurate classification are extracted using dense cross-connected residual blocks. Spatial attention is provided to the network so that the spatial information is not lost during the feature extraction. DeepHistoNet is trained and evaluated on the liver, lung, and colon histopathology datasets to demonstrate its resilience. The results are promising and outperform state-of-the-art techniques. The proposed methodology has obtained the AUC-ROC value of 0.9867 with a classification accuracy of 97.1% on the KMC dataset. The proposed DeepHistoNet has classified the LC25000 dataset with 99.8% accuracy. The results are the best obtained till date.
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Affiliation(s)
| | - Deivalakshmi S
- Department of ECE, National Institute of Technology, Tiruchirappalli, India
| | - Pandeeswari R
- Department of ECE, National Institute of Technology, Tiruchirappalli, India
| | - Seok-Bum Ko
- Department of Electrical and Computer, Biomedical Engineering, University of Saskatchewan, Saskatoon, Canada
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Haq I, Mazhar T, Asif RN, Ghadi YY, Ullah N, Khan MA, Al-Rasheed A. YOLO and residual network for colorectal cancer cell detection and counting. Heliyon 2024; 10:e24403. [PMID: 38304780 PMCID: PMC10831604 DOI: 10.1016/j.heliyon.2024.e24403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2023] [Revised: 12/30/2023] [Accepted: 01/08/2024] [Indexed: 02/03/2024] Open
Abstract
The HT-29 cell line, derived from human colon cancer, is valuable for biological and cancer research applications. Early detection is crucial for improving the chances of survival, and researchers are introducing new techniques for accurate cancer diagnosis. This study introduces an efficient deep learning-based method for detecting and counting colorectal cancer cells (HT-29). The colorectal cancer cell line was procured from a company. Further, the cancer cells were cultured, and a transwell experiment was conducted in the lab to collect the dataset of colorectal cancer cell images via fluorescence microscopy. Of the 566 images, 80 % were allocated to the training set, and the remaining 20 % were assigned to the testing set. The HT-29 cell detection and counting in medical images is performed by integrating YOLOv2, ResNet-50, and ResNet-18 architectures. The accuracy achieved by ResNet-18 is 98.70 % and ResNet-50 is 96.66 %. The study achieves its primary objective by focusing on detecting and quantifying congested and overlapping colorectal cancer cells within the images. This innovative work constitutes a significant development in overlapping cancer cell detection and counting, paving the way for novel advancements and opening new avenues for research and clinical applications. Researchers can extend the study by exploring variations in ResNet and YOLO architectures to optimize object detection performance. Further investigation into real-time deployment strategies will enhance the practical applicability of these models.
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Affiliation(s)
- Inayatul Haq
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Tehseen Mazhar
- Department of Computer Science, Virtual University of Pakistan, Lahore, 55150, Pakistan
| | - Rizwana Naz Asif
- School of Computer Science, National College of Business Administration and Economics, Lahore, 54000, Pakistan
| | - Yazeed Yasin Ghadi
- Department of Computer Science and Software Engineering, Al Ain University, Abu Dhabi, 12555, United Arab Emirates
| | - Najib Ullah
- Faculty of Pharmacy and Health Sciences, Department of Pharmacy, University of Balochistan, Quetta, 08770, Pakistan
| | - Muhammad Amir Khan
- School of Computing Sciences, College of Computing, Informatics and Mathematics, Universiti Teknologi MARA, 40450, Shah Alam, Selangor, Malaysia
| | - Amal Al-Rasheed
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
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14
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Muniasamy A, Alquhtani SAS, Bilfaqih SM, Balaji P, Karunakaran G. Lung cancer histopathology image classification using transfer learning with convolution neural network model. Technol Health Care 2024; 32:1199-1210. [PMID: 37270826 DOI: 10.3233/thc-231029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/23/2024]
Abstract
BACKGROUND Lung cancer (LC) is a harmful malignant tumor and potentially lethal illness. Therefore, early detection of LC is an urgent need, and dependent on the type of histology and the type of disease. The use of deep learning algorithms (DL) is required to analyse the histopathology images of LC and make treatment decisions accordingly. OBJECTIVE This study aimed to apply pretrained EfficientNetB7 model to facilitate the process of classifying LC histopathology images as primary malignancy categories (adenocarcinoma, squamous cell carcinoma and large cell carcinoma) for early treatment of LC patients. Also, aims to analyse the performance of the proposed model using the accuracy measure. METHODS The dataset of 15000 histopathology images of lung cancer were examined. EfficientNetB7, a special type of convolution neural network (CNN), pretrained with ImageNet for transfer learning were trained on this dataset. Accuracy metric was used for the evaluation of the proposed model RESULTS The feature extraction was performed by applying transfer learning using EfficientNetB7 as pretrained model. The proposed model achieved 99.77% accuracy, while previous studies model achieved over 90 to 99% accuracy. CONCLUSION The employment of CNN based EfficientNetB7 model for the classification of LC based on histopathology images can speed up the diagnosis of LC and reduce the burden on pathologists for the early treatment of patients.
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Haq I, Mazhar T, Naz Asif R, Yasin Ghadi Y, Saleem R, Mallek F, Hamam H. A deep learning approach for the detection and counting of colon cancer cells (HT-29 cells) bunches and impurities. PeerJ Comput Sci 2023; 9:e1651. [PMID: 38192457 PMCID: PMC10773923 DOI: 10.7717/peerj-cs.1651] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 09/22/2023] [Indexed: 01/10/2024]
Abstract
HT-29 has an epithelial appearance as a human colorectal cancer cell line. Early detection of colorectal cancer can enhance survival rates. This study aims to detect and count HT-29 cells using a deep-learning approach (ResNet-50). The cell lines were procured from Procell Life Science & Technology Co., Ltd. (Wuhan, China). Further, the dataset is self-prepared in lab experiments, cell culture, and collected 566 images. These images contain two classes; the HT-29 human colorectal adenocarcinoma cells (blue shapes in bunches) and impurities (tinny circular grey shapes). These images are annotated with the help of an image labeller as impurity and cancer cells. Then afterwards, the images are trained, validated, and tested against the deep learning approach ResNet50. Finally, in each image, the number of impurity and cancer cells are counted to find the accuracy of the proposed model. Accuracy and computational expense are used to gauge the network's performance. Each model is tested ten times with a non-overlapping train and random test splits. The effect of data pre-processing is also examined and shown in several tasks. The results show an accuracy of 95.5% during training and 95.3% in validation for detecting and counting HT-29 cells. HT-29 cell detection and counting using deep learning is novel due to the scarcity of research in this area, the application of deep learning, and potential performance improvements over traditional methods. By addressing a gap in the literature, employing a unique dataset, and using custom model architecture, this approach contributes to advancing colon cancer understanding and diagnosis techniques.
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Affiliation(s)
- Inayatul Haq
- School of Electrical and Information Engineering, Zhengzhou University, Zhengzhou, Henan, China
| | - Tehseen Mazhar
- Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan
| | - Rizwana Naz Asif
- School of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan
| | - Yazeed Yasin Ghadi
- Department of computer science and software engineering, Al Ain university, Abu Dhabi, United Arab Emirates
| | - Rabea Saleem
- Department of computer science and software engineering, Air University, Multan, Pakistan
| | - Fatma Mallek
- Faculty of Engineering, University of Moncton, Moncton, Canada
| | - Habib Hamam
- Faculty of Engineering, University of Moncton, Moncton, Canada
- Spectrum of Knowledge Production, Skills Development, Sfax, Tunisia
- College of Computer Science and Engineering, University of Ha’il, Ha’il, Saudi Arabia
- International Institute of Technology and Management, Libreville, Commune d’Akanda, Gabon
- Department of Electrical and Electronic Engineering Science, School of Electrical Engineering, University of Johannesburg, Johannesburg, South Africa
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16
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Zughaibi TA, Jabir NR, Khan AU, Khan MS, Tabrez S. Screening of Cu 4 O 3 NPs efficacy and its anticancer potential against cervical cancer. Cell Biochem Funct 2023; 41:1174-1187. [PMID: 37691077 DOI: 10.1002/cbf.3850] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 07/31/2023] [Accepted: 08/26/2023] [Indexed: 09/12/2023]
Abstract
Cu4 O3 is the least explored copper oxide, and its nanoformulation is anticipated to have important therapeutic potential especially against cancer. The current study aimed to biosynthesize Cu4 O3 nanoparticles (NPs) using an aqueous extract of pumpkin seeds and evaluate its antiproliferative efficacy against cervical cells after screening on different cancer cell lines. The obtained NPs were characterized by different spectroscopic analyses, such as UV-vis, thermogravimetric, energy dispersive X-ray, and Fourier-transform infrared spectroscopy (FTIR). In addition, high-resolution transmission electron microscopes (HR-TEM) were used to observe the morphology of the biosynthesized NPs. The UV-vis spectra showed a peak at around 332 nm, confirming the formation of Cu4 O3 NPs. Moreover, FTIR and TAG analyses identified the presence of various bioactive phytoconstituents that might have worked as capping and stabilization agents and comparative stable NPs at very high temperatures, respectively. The HR-TEM data showed the spherical shape of Cu4 O3 NPs in the range of 100 nm. The Cu4 O3 NPs was screened on three different cancer cell lines viz., Hela, MDA-MB-231, and HCT-116 using cytotoxicity (MTT) reduction assay. In addition, Vero was taken as a normal epithelial (control) cell. The high responsive cell line in terms of least IC50 was further assessed for its anticancer potential using a battery of biological tests, including morphological alterations, induction of apoptosis/ROS generation, regulation of mitochondrial membrane potential (MMP), and suppression of cell adhesion/migration. Vero cells (control) showed a slight decline in % cell viability even at the highest tested Cu4 O3 NPs concentration. However, all the studied cancer cells viz., MDA-MB-231, HCT 116, and HeLa cells showed a dose-dependent decline in cell viability after the treatment with Cu4 O3 NPs with a calculated IC50 value of 10, 11, and 7.2 µg/mL, respectively. Based on the above data, Hela cells were chosen for further studies, that showed induction of apoptosis from 3.5 to 9-folds by three different staining techniques acridine orange/ethidium bromide (AO/EB), 4',6-diamidino-2-phenylindole (DAPI), and propidium iodide (PI). The enhanced production of reactive oxygen species (>3.5-fold), modulation in MMP, and suppression of cell adhesion/migration were observed in the cells treated with Cu4 O3 NPs. The current study obtained the significant antiproliferative potential of Cu4 O3 NPs against the cervical cancer cell line, which needs to be confirmed further in a suitable in vivo model. Based on our results, we also recommend the green-based, eco-friendly, and cost-effective alternative method for synthesizing novel nanoformulation.
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Affiliation(s)
- Torki A Zughaibi
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Nasimudeen R Jabir
- Department of Biochemistry, Centre for Research and Development, PRIST University, Vallam Thanjavur, Tamil Nadu, India
| | - Azhar U Khan
- Department of Chemistry, School of Life and Basic Sciences, Siilas Campus, Jaipur National University, Jaipur, Rajasthan, India
| | - Mohd Shahnawaz Khan
- Protein Research Chair, Department of Biochemistry, College of Science, King Saud University, Riyadh, Saudi Arabia
| | - Shams Tabrez
- King Fahd Medical Research Center, King Abdulaziz University, Jeddah, Saudi Arabia
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia
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Adiwinata R, Tandarto K, Arifputra J, Waleleng BJ, Gosal F, Rotty L, Winarta J, Waleleng A, Simadibrata P, Simadibrata M. The Impact of Artificial Intelligence in Improving Polyp and Adenoma Detection Rate During Colonoscopy: Systematic-Review and Meta-Analysis. Asian Pac J Cancer Prev 2023; 24:3655-3663. [PMID: 38019222 PMCID: PMC10772777 DOI: 10.31557/apjcp.2023.24.11.3655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 11/10/2023] [Indexed: 11/30/2023] Open
Abstract
INTRODUCTION Colonoscopy may detect colorectal polyp and facilitate its removal in order to prevent colorectal cancer. However, substantial miss rate for colorectal adenomas detection still occurred during screening colonoscopy procedure. Nowadays, artificial intelligence (AI) have been employed in trials to improve polyp detection rate (PDR) and adenoma detection rate (ADR). Therefore, we would like to determine the impact of AI in increasing PDR and ADR. METHODS The present study adhered to the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-analyses 2020 (PRISMA 2020) statement. To identify relevant literature, comprehensive searches were conducted on major scientific databases, including Pubmed, EBSCO-host, and Proquest. The search was limited to articles published up to November 30, 2022. Inclusion criteria for the study encompassed full-text accessibility, articles written in the English language, and randomized controlled trials (RCTs) that reported both ADR and PDR values, comparing conventional diagnostic methods with AI-aided approaches. To synthesize the data, we computed the combined pooled odds ratio (OR) using a random-effects model. This model was chosen due to the expectation of considerable heterogeneity among the selected studies. To evaluate potential publication bias, the Begg's funnel diagram was employed. RESULTS A total of 13 studies were included in this study. Colonoscopy with AI had significantly higher PDR compared to without AI (pooled OR 1.46, 95% CI 1.13-1.89, p = 0.003) and higher ADR (pooled OR 1.58, 95% CI 1.37-1.82, p < 0.00001). PDR analysis showed moderate heterogeneity between included studies (p = 0.004; I2=63%). Furthermore, ADR analysis showed moderate heterogeneity (p < 0.007; I2 = 57%). Additionally, the funnels plot of ADR and PDR analysis showed an asymmetry plot and low publication bias. CONCLUSION AI may improve colonoscopy result quality through improving PDR and ADR.
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Affiliation(s)
- Randy Adiwinata
- Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Kevin Tandarto
- S.K Lerik Regional Public Hospital, Kupang, East Nusa Tenggara, Indonesia.
| | - Jonathan Arifputra
- Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Bradley Jimmy Waleleng
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Fandy Gosal
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Luciana Rotty
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Jeanne Winarta
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Andrew Waleleng
- Division of Gastroenterology-Hepatology, Department of Internal Medicine, Faculty of Medicine, Universitas Sam Ratulangi/Prof. dr. R. D. Kandou Hospital, Manado, Indonesia.
| | - Paulus Simadibrata
- Department of Internal Medicine, Abdi Waluyo Hospital, Jakarta, Indonesia.
| | - Marcellus Simadibrata
- Division of Gastroenterology, Pancreatobiliary and Digestive Endoscopy, Department of Internal Medicine, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo General Hospital, Jakarta, Indonesia.
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Rai HM, Yoo J. A comprehensive analysis of recent advancements in cancer detection using machine learning and deep learning models for improved diagnostics. J Cancer Res Clin Oncol 2023; 149:14365-14408. [PMID: 37540254 DOI: 10.1007/s00432-023-05216-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2023] [Accepted: 07/26/2023] [Indexed: 08/05/2023]
Abstract
PURPOSE There are millions of people who lose their life due to several types of fatal diseases. Cancer is one of the most fatal diseases which may be due to obesity, alcohol consumption, infections, ultraviolet radiation, smoking, and unhealthy lifestyles. Cancer is abnormal and uncontrolled tissue growth inside the body which may be spread to other body parts other than where it has originated. Hence it is very much required to diagnose the cancer at an early stage to provide correct and timely treatment. Also, manual diagnosis and diagnostic error may cause of the death of many patients hence much research are going on for the automatic and accurate detection of cancer at early stage. METHODS In this paper, we have done the comparative analysis of the diagnosis and recent advancement for the detection of various cancer types using traditional machine learning (ML) and deep learning (DL) models. In this study, we have included four types of cancers, brain, lung, skin, and breast and their detection using ML and DL techniques. In extensive review we have included a total of 130 pieces of literature among which 56 are of ML-based and 74 are from DL-based cancer detection techniques. Only the peer reviewed research papers published in the recent 5-year span (2018-2023) have been included for the analysis based on the parameters, year of publication, feature utilized, best model, dataset/images utilized, and best accuracy. We have reviewed ML and DL-based techniques for cancer detection separately and included accuracy as the performance evaluation metrics to maintain the homogeneity while verifying the classifier efficiency. RESULTS Among all the reviewed literatures, DL techniques achieved the highest accuracy of 100%, while ML techniques achieved 99.89%. The lowest accuracy achieved using DL and ML approaches were 70% and 75.48%, respectively. The difference in accuracy between the highest and lowest performing models is about 28.8% for skin cancer detection. In addition, the key findings, and challenges for each type of cancer detection using ML and DL techniques have been presented. The comparative analysis between the best performing and worst performing models, along with overall key findings and challenges, has been provided for future research purposes. Although the analysis is based on accuracy as the performance metric and various parameters, the results demonstrate a significant scope for improvement in classification efficiency. CONCLUSION The paper concludes that both ML and DL techniques hold promise in the early detection of various cancer types. However, the study identifies specific challenges that need to be addressed for the widespread implementation of these techniques in clinical settings. The presented results offer valuable guidance for future research in cancer detection, emphasizing the need for continued advancements in ML and DL-based approaches to improve diagnostic accuracy and ultimately save more lives.
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Affiliation(s)
- Hari Mohan Rai
- School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, 13120, Gyeonggi-do, Republic of Korea.
| | - Joon Yoo
- School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-gu, Seongnam-si, 13120, Gyeonggi-do, Republic of Korea
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Rajinikanth V, Kadry S, Mohan R, Rama A, Khan MA, Kim J. Colon histology slide classification with deep-learning framework using individual and fused features. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:19454-19467. [PMID: 38052609 DOI: 10.3934/mbe.2023861] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
Cancer occurrence rates are gradually rising in the population, which reasons a heavy diagnostic burden globally. The rate of colorectal (bowel) cancer (CC) is gradually rising, and is currently listed as the third most common cancer globally. Therefore, early screening and treatments with a recommended clinical protocol are necessary to trat cancer. The proposed research aim of this paper to develop a Deep-Learning Framework (DLF) to classify the colon histology slides into normal/cancer classes using deep-learning-based features. The stages of the framework include the following: (ⅰ) Image collection, resizing, and pre-processing; (ⅱ) Deep-Features (DF) extraction with a chosen scheme; (ⅲ) Binary classification with a 5-fold cross-validation; and (ⅳ) Verification of the clinical significance. This work classifies the considered image database using the follwing: (ⅰ) Individual DF, (ⅱ) Fused DF, and (ⅲ) Ensemble DF. The achieved results are separately verified using binary classifiers. The proposed work considered 4000 (2000 normal and 2000 cancer) histology slides for the examination. The result of this research confirms that the fused DF helps to achieve a detection accuracy of 99% with the K-Nearest Neighbor (KNN) classifier. In contrast, the individual and ensemble DF provide classification accuracies of 93.25 and 97.25%, respectively.
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Affiliation(s)
- Venkatesan Rajinikanth
- Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai 602105, India
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, Ajman 346, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 1401, Lebanon
| | - Ramya Mohan
- Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai 602105, India
| | - Arunmozhi Rama
- Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, SIMATS, Chennai 602105, India
| | - Muhammad Attique Khan
- Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon
| | - Jungeun Kim
- Department of Software, Kongju National University, Cheonan, 31080, Korea
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Jing Y, Li C, Du T, Jiang T, Sun H, Yang J, Shi L, Gao M, Grzegorzek M, Li X. A comprehensive survey of intestine histopathological image analysis using machine vision approaches. Comput Biol Med 2023; 165:107388. [PMID: 37696178 DOI: 10.1016/j.compbiomed.2023.107388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 08/06/2023] [Accepted: 08/25/2023] [Indexed: 09/13/2023]
Abstract
Colorectal Cancer (CRC) is currently one of the most common and deadly cancers. CRC is the third most common malignancy and the fourth leading cause of cancer death worldwide. It ranks as the second most frequent cause of cancer-related deaths in the United States and other developed countries. Histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of CRC. In order to improve the objectivity and diagnostic efficiency for image analysis of intestinal histopathology, Computer-aided Diagnosis (CAD) methods based on machine learning (ML) are widely applied in image analysis of intestinal histopathology. In this investigation, we conduct a comprehensive study on recent ML-based methods for image analysis of intestinal histopathology. First, we discuss commonly used datasets from basic research studies with knowledge of intestinal histopathology relevant to medicine. Second, we introduce traditional ML methods commonly used in intestinal histopathology, as well as deep learning (DL) methods. Then, we provide a comprehensive review of the recent developments in ML methods for segmentation, classification, detection, and recognition, among others, for histopathological images of the intestine. Finally, the existing methods have been studied, and the application prospects of these methods in this field are given.
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Affiliation(s)
- Yujie Jing
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Chen Li
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.
| | - Tianming Du
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Tao Jiang
- School of Intelligent Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, China; International Joint Institute of Robotics and Intelligent Systems, Chengdu University of Information Technology, Chengdu, China
| | - Hongzan Sun
- Shengjing Hospital of China Medical University, Shenyang, China
| | - Jinzhu Yang
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Liyu Shi
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Minghe Gao
- Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, China; Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China
| | - Marcin Grzegorzek
- Institute for Medical Informatics, University of Luebeck, Luebeck, Germany; Department of Knowledge Engineering, University of Economics in Katowice, Katowice, Poland
| | - Xiaoyan Li
- Cancer Hospital of China Medical University, Liaoning Cancer Hospital, Shenyang, China.
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Zheng Z, Yao H, Lin C, Huang K, Chen L, Shao Z, Zhou H, Zhao G. KD_ConvNeXt: knowledge distillation-based image classification of lung tumor surgical specimen sections. Front Genet 2023; 14:1254435. [PMID: 37790704 PMCID: PMC10544998 DOI: 10.3389/fgene.2023.1254435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Accepted: 08/10/2023] [Indexed: 10/05/2023] Open
Abstract
Introduction: Lung cancer is currently among the most prevalent and lethal cancers in the world in terms of incidence and fatality rates. In clinical practice, identifying the specific subtypes of lung cancer is essential in diagnosing and treating lung lesions. Methods: This paper aims to collect histopathological section images of lung tumor surgical specimens to construct a clinical dataset for researching and addressing the classification problem of specific subtypes of lung tumors. Our method proposes a teacher-student network architecture based on a knowledge distillation mechanism for the specific subtype classification of lung tumor histopathological section images to assist clinical applications, namely KD_ConvNeXt. The proposed approach enables the student network (ConvNeXt) to extract knowledge from the intermediate feature layers of the teacher network (Swin Transformer), improving the feature extraction and fitting capabilities of ConvNeXt. Meanwhile, Swin Transformer provides soft labels containing information about the distribution of images in various categories, making the model focused more on the information carried by types with smaller sample sizes while training. Results: This work has designed many experiments on a clinical lung tumor image dataset, and the KD_ConvNeXt achieved a superior classification accuracy of 85.64% and an F1-score of 0.7717 compared with other advanced image classification methods.
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Affiliation(s)
- Zhaoliang Zheng
- South China Normal University, Guangzhou, China
- Key Lab on Cloud Security and Assessment Technology of Guangzhou, Guangzhou, China
- SCNU & VeChina Joint Lab on BlockChain Technology and Application, Guangzhou, China
| | - Henian Yao
- The First School of Clinical Medicine, Guangdong Medical University, Zhanjiang, China
- Department of Thoracic Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Chengchuang Lin
- South China Normal University, Guangzhou, China
- Key Lab on Cloud Security and Assessment Technology of Guangzhou, Guangzhou, China
- SCNU & VeChina Joint Lab on BlockChain Technology and Application, Guangzhou, China
| | - Kaixin Huang
- South China Normal University, Guangzhou, China
- Key Lab on Cloud Security and Assessment Technology of Guangzhou, Guangzhou, China
- SCNU & VeChina Joint Lab on BlockChain Technology and Application, Guangzhou, China
| | - Luoxuan Chen
- South China Normal University, Guangzhou, China
- Key Lab on Cloud Security and Assessment Technology of Guangzhou, Guangzhou, China
- SCNU & VeChina Joint Lab on BlockChain Technology and Application, Guangzhou, China
| | - Ziling Shao
- Jinan University-University of Birmingham Joint Institute at Jinan University, Guangdong, China
| | - Haiyu Zhou
- The First School of Clinical Medicine, Guangdong Medical University, Zhanjiang, China
- Department of Thoracic Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China
| | - Gansen Zhao
- South China Normal University, Guangzhou, China
- Key Lab on Cloud Security and Assessment Technology of Guangzhou, Guangzhou, China
- SCNU & VeChina Joint Lab on BlockChain Technology and Application, Guangzhou, China
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22
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Gabralla LA, Hussien AM, AlMohimeed A, Saleh H, Alsekait DM, El-Sappagh S, Ali AA, Refaat Hassan M. Automated Diagnosis for Colon Cancer Diseases Using Stacking Transformer Models and Explainable Artificial Intelligence. Diagnostics (Basel) 2023; 13:2939. [PMID: 37761306 PMCID: PMC10529133 DOI: 10.3390/diagnostics13182939] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/23/2023] [Accepted: 08/31/2023] [Indexed: 09/29/2023] Open
Abstract
Colon cancer is the third most common cancer type worldwide in 2020, almost two million cases were diagnosed. As a result, providing new, highly accurate techniques in detecting colon cancer leads to early and successful treatment of this disease. This paper aims to propose a heterogenic stacking deep learning model to predict colon cancer. Stacking deep learning is integrated with pretrained convolutional neural network (CNN) models with a metalearner to enhance colon cancer prediction performance. The proposed model is compared with VGG16, InceptionV3, Resnet50, and DenseNet121 using different evaluation metrics. Furthermore, the proposed models are evaluated using the LC25000 and WCE binary and muticlassified colon cancer image datasets. The results show that the stacking models recorded the highest performance for the two datasets. For the LC25000 dataset, the stacked model recorded the highest performance accuracy, recall, precision, and F1 score (100). For the WCE colon image dataset, the stacked model recorded the highest performance accuracy, recall, precision, and F1 score (98). Stacking-SVM achieved the highest performed compared to existing models (VGG16, InceptionV3, Resnet50, and DenseNet121) because it combines the output of multiple single models and trains and evaluates a metalearner using the output to produce better predictive results than any single model. Black-box deep learning models are represented using explainable AI (XAI).
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Affiliation(s)
- Lubna Abdelkareim Gabralla
- Department of Computer Science and Information Technology, Applied College, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Ali Mohamed Hussien
- Department of Computer Science, Faculty of Science, Aswan University, Aswan 81528, Egypt
| | - Abdulaziz AlMohimeed
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
| | - Hager Saleh
- Faculty of Computers and Artificial Intelligence, South Valley University, Hurghada 84511, Egypt
| | - Deema Mohammed Alsekait
- Department of Computer Science and Information Technology, Applied College, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
| | - Shaker El-Sappagh
- Faculty of Computer Science and Engineering, Galala University, Suez 34511, Egypt
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Banha 13518, Egypt
| | - Abdelmgeid A. Ali
- Faculty of Computers and Information, Minia University, Minia 61519, Egypt
| | - Moatamad Refaat Hassan
- Department of Computer Science, Faculty of Science, Aswan University, Aswan 81528, Egypt
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23
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Rai HM. Cancer detection and segmentation using machine learning and deep learning techniques: a review. MULTIMEDIA TOOLS AND APPLICATIONS 2023. [DOI: 10.1007/s11042-023-16520-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/16/2022] [Revised: 05/12/2023] [Accepted: 08/13/2023] [Indexed: 09/16/2023]
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24
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Iqbal S, Qureshi AN, Alhussein M, Aurangzeb K, Kadry S. A Novel Heteromorphous Convolutional Neural Network for Automated Assessment of Tumors in Colon and Lung Histopathology Images. Biomimetics (Basel) 2023; 8:370. [PMID: 37622975 PMCID: PMC10452605 DOI: 10.3390/biomimetics8040370] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/26/2023] Open
Abstract
The automated assessment of tumors in medical image analysis encounters challenges due to the resemblance of colon and lung tumors to non-mitotic nuclei and their heteromorphic characteristics. An accurate assessment of tumor nuclei presence is crucial for determining tumor aggressiveness and grading. This paper proposes a new method called ColonNet, a heteromorphous convolutional neural network (CNN) with a feature grafting methodology categorically configured for analyzing mitotic nuclei in colon and lung histopathology images. The ColonNet model consists of two stages: first, identifying potential mitotic patches within the histopathological imaging areas, and second, categorizing these patches into squamous cell carcinomas, adenocarcinomas (lung), benign (lung), benign (colon), and adenocarcinomas (colon) based on the model's guidelines. We develop and employ our deep CNNs, each capturing distinct structural, textural, and morphological properties of tumor nuclei, to construct the heteromorphous deep CNN. The execution of the proposed ColonNet model is analyzed by its comparison with state-of-the-art CNNs. The results demonstrate that our model surpasses others on the test set, achieving an impressive F1 score of 0.96, sensitivity and specificity of 0.95, and an area under the accuracy curve of 0.95. These outcomes underscore our hybrid model's superior performance, excellent generalization, and accuracy, highlighting its potential as a valuable tool to support pathologists in diagnostic activities.
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Affiliation(s)
- Saeed Iqbal
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore 54000, Pakistan;
| | - Adnan N. Qureshi
- Department of Computer Science, Faculty of Information Technology & Computer Science, University of Central Punjab, Lahore 54000, Pakistan;
| | - Musaed Alhussein
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia; (M.A.); (K.A.)
| | - Khursheed Aurangzeb
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia; (M.A.); (K.A.)
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway;
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25
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Ren Z, Kong X, Zhang Y, Wang S. UKSSL: Underlying Knowledge Based Semi-Supervised Learning for Medical Image Classification. IEEE OPEN JOURNAL OF ENGINEERING IN MEDICINE AND BIOLOGY 2023; 5:459-466. [PMID: 38899016 PMCID: PMC11186655 DOI: 10.1109/ojemb.2023.3305190] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 07/10/2023] [Accepted: 07/23/2023] [Indexed: 06/21/2024] Open
Abstract
Goal: Deep learning techniques have made significant progress in medical image analysis. However, obtaining ground truth labels for unlabeled medical images is challenging as they often outnumber labeled images. Thus, training a high-performance model with limited labeled data has become a crucial challenge. Methods: This study introduces an underlying knowledge-based semi-supervised framework called UKSSL, consisting of two components: MedCLR extracts feature representations from the unlabeled dataset; UKMLP utilizes the representation and fine-tunes it with the limited labeled dataset to classify the medical images. Results: UKSSL evaluates on the LC25000 and BCCD datasets, using only 50% labeled data. It gets precision, recall, F1-score, and accuracy of 98.9% on LC25000 and 94.3%, 94.5%, 94.3%, and 94.1% on BCCD, respectively. These results outperform other supervised-learning methods using 100% labeled data. Conclusions: The UKSSL can efficiently extract underlying knowledge from the unlabeled dataset and perform better using limited labeled medical images.
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Grants
- MRC, U.K.
- Royal Society, U.K
- BHF, U.K.
- Hope Foundation for Cancer Research, U.K.
- GCRF, U.K.
- Sino-U.K. Industrial Fund, U.K.
- LIAS, U.K.
- Data Science Enhancement Fund, U.K.
- Fight for Sight, U.K.
- Sino-U.K. Education Fund, U.K.
- BBSRC, U.K.
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Affiliation(s)
- Zeyu Ren
- University of LeicesterLE1 7RHLeicesterU.K.
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26
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Chlorogiannis DD, Verras GI, Tzelepi V, Chlorogiannis A, Apostolos A, Kotis K, Anagnostopoulos CN, Antzoulas A, Davakis S, Vailas M, Schizas D, Mulita F. Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation? PRZEGLAD GASTROENTEROLOGICZNY 2023; 18:353-367. [PMID: 38572457 PMCID: PMC10985751 DOI: 10.5114/pg.2023.130337] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 05/20/2023] [Indexed: 04/05/2024]
Abstract
Colorectal cancer is one of the most prevalent types of cancer, with histopathologic examination of biopsied tissue samples remaining the gold standard for diagnosis. During the past years, artificial intelligence (AI) has steadily found its way into the field of medicine and pathology, especially with the introduction of whole slide imaging (WSI). The main outcome of interest was the composite balanced accuracy (ACC) as well as the F1 score. The average reported ACC from the collected studies was 95.8 ±3.8%. Reported F1 scores reached as high as 0.975, with an average of 89.7 ±9.8%, indicating that existing deep learning algorithms can achieve in silico distinction between malignant and benign. Overall, the available state-of-the-art algorithms are non-inferior to pathologists for image analysis and classification tasks. However, due to their inherent uniqueness in their training and lack of widely accepted external validation datasets, their generalization potential is still limited.
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Affiliation(s)
| | | | - Vasiliki Tzelepi
- Department of Pathology, School of Medicine, University of Patras, Patras, Greece
| | | | - Anastasios Apostolos
- First Department of Cardiology, Hippokration Hospital, University of Athens, Athens, Greece
| | - Konstantinos Kotis
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
| | | | - Andreas Antzoulas
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | - Spyridon Davakis
- Upper Gastrointestinal and General Surgery Unit, First Department of Surgery, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
| | - Michail Vailas
- Upper Gastrointestinal and General Surgery Unit, First Department of Surgery, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
| | - Dimitrios Schizas
- Upper Gastrointestinal and General Surgery Unit, First Department of Surgery, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
| | - Francesk Mulita
- Department of Surgery, General University Hospital of Patras, Patras, Greece
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27
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Davri A, Birbas E, Kanavos T, Ntritsos G, Giannakeas N, Tzallas AT, Batistatou A. Deep Learning for Lung Cancer Diagnosis, Prognosis and Prediction Using Histological and Cytological Images: A Systematic Review. Cancers (Basel) 2023; 15:3981. [PMID: 37568797 PMCID: PMC10417369 DOI: 10.3390/cancers15153981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/27/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023] Open
Abstract
Lung cancer is one of the deadliest cancers worldwide, with a high incidence rate, especially in tobacco smokers. Lung cancer accurate diagnosis is based on distinct histological patterns combined with molecular data for personalized treatment. Precise lung cancer classification from a single H&E slide can be challenging for a pathologist, requiring most of the time additional histochemical and special immunohistochemical stains for the final pathology report. According to WHO, small biopsy and cytology specimens are the available materials for about 70% of lung cancer patients with advanced-stage unresectable disease. Thus, the limited available diagnostic material necessitates its optimal management and processing for the completion of diagnosis and predictive testing according to the published guidelines. During the new era of Digital Pathology, Deep Learning offers the potential for lung cancer interpretation to assist pathologists' routine practice. Herein, we systematically review the current Artificial Intelligence-based approaches using histological and cytological images of lung cancer. Most of the published literature centered on the distinction between lung adenocarcinoma, lung squamous cell carcinoma, and small cell lung carcinoma, reflecting the realistic pathologist's routine. Furthermore, several studies developed algorithms for lung adenocarcinoma predominant architectural pattern determination, prognosis prediction, mutational status characterization, and PD-L1 expression status estimation.
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Affiliation(s)
- Athena Davri
- Department of Pathology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45500 Ioannina, Greece;
| | - Effrosyni Birbas
- Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (E.B.); (T.K.)
| | - Theofilos Kanavos
- Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece; (E.B.); (T.K.)
| | - Georgios Ntritsos
- Department of Hygiene and Epidemiology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45110 Ioannina, Greece;
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Nikolaos Giannakeas
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Alexandros T. Tzallas
- Department of Informatics and Telecommunications, University of Ioannina, 47100 Arta, Greece;
| | - Anna Batistatou
- Department of Pathology, Faculty of Medicine, School of Health Sciences, University of Ioannina, 45500 Ioannina, Greece;
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28
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Javed MA, Bin Liaqat H, Meraj T, Alotaibi A, Alshammari M. Identification and Classification of Lungs Focal Opacity Using CNN Segmentation and Optimal Feature Selection. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2023; 2023:6357252. [PMID: 37538561 PMCID: PMC10396675 DOI: 10.1155/2023/6357252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 09/07/2022] [Accepted: 09/26/2022] [Indexed: 08/05/2023]
Abstract
Lung cancer is one of the deadliest cancers around the world, with high mortality rate in comparison to other cancers. A lung cancer patient's survival probability in late stages is very low. However, if it can be detected early, the patient survival rate can be improved. Diagnosing lung cancer early is a complicated task due to having the visual similarity of lungs nodules with trachea, vessels, and other surrounding tissues that leads toward misclassification of lung nodules. Therefore, correct identification and classification of nodules is required. Previous studies have used noisy features, which makes results comprising. A predictive model has been proposed to accurately detect and classify the lung nodules to address this problem. In the proposed framework, at first, the semantic segmentation was performed to identify the nodules in images in the Lungs image database consortium (LIDC) dataset. Optimal features for classification include histogram oriented gradients (HOGs), local binary patterns (LBPs), and geometric features are extracted after segmentation of nodules. The results shown that support vector machines performed better in identifying the nodules than other classifiers, achieving the highest accuracy of 97.8% with sensitivity of 100%, specificity of 93%, and false positive rate of 6.7%.
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Affiliation(s)
| | - Hannan Bin Liaqat
- Department of Information Technology, Division of Science and Technology University of Education, Township Campus Lahore, Lahore, Pakistan
| | - Talha Meraj
- Department of Computer Science, COMSATS University Islamabad—Wah Campus, Wah Cantt, Rawalpindi 47040, Pakistan
| | - Aziz Alotaibi
- Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
| | - Majid Alshammari
- Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
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29
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Hamed EAR, Salem MAM, Badr NL, Tolba MF. An Efficient Combination of Convolutional Neural Network and LightGBM Algorithm for Lung Cancer Histopathology Classification. Diagnostics (Basel) 2023; 13:2469. [PMID: 37568831 PMCID: PMC10416893 DOI: 10.3390/diagnostics13152469] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 07/14/2023] [Accepted: 07/17/2023] [Indexed: 08/13/2023] Open
Abstract
The most dangerous disease in recent decades is lung cancer. The most accurate method of cancer diagnosis, according to research, is through the use of histopathological images that are acquired by a biopsy. Deep learning techniques have achieved success in bioinformatics, particularly medical imaging. In this paper, we present an innovative method for rapidly identifying and classifying histopathology images of lung tissues by combining a newly proposed Convolutional Neural Networks (CNN) model with a few total parameters and the enhanced Light Gradient Boosting Model (LightGBM) classifier. After the images have been pre-processed in this study, the proposed CNN technique is provided for feature extraction. Then, the LightGBM model with multiple threads has been used for lung tissue classification. The simulation result, applied to the LC25000 dataset, demonstrated that the novel technique successfully classifies lung tissue with 99.6% accuracy and sensitivity. Furthermore, the proposed CNN model has achieved the lowest training parameters of only one million parameters, and it has also achieved the shortest processing time of just one second throughout the feature extraction process. When this result is compared with the most recent state-of-the-art approaches, the suggested approach has increased effectiveness in the areas of both disease classification accuracy and processing time.
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Affiliation(s)
- Esraa A.-R. Hamed
- Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt; (N.L.B.); (M.F.T.)
| | - Mohammed A.-M. Salem
- Media Engineering and Technology, German University in Cairo (GUC), Cairo 16482, Egypt;
| | - Nagwa L. Badr
- Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt; (N.L.B.); (M.F.T.)
| | - Mohamed F. Tolba
- Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt; (N.L.B.); (M.F.T.)
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30
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Bousis D, Verras GI, Bouchagier K, Antzoulas A, Panagiotopoulos I, Katinioti A, Kehagias D, Kaplanis C, Kotis K, Anagnostopoulos CN, Mulita F. The role of deep learning in diagnosing colorectal cancer. PRZEGLAD GASTROENTEROLOGICZNY 2023; 18:266-273. [PMID: 37937113 PMCID: PMC10626379 DOI: 10.5114/pg.2023.129494] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 02/24/2023] [Indexed: 11/09/2023]
Abstract
Colon cancer is a major public health issue, affecting a growing number of individuals worldwide. Proper and early diagnosis of colon cancer is the necessary first step toward effective treatment and/or prevention of future disease relapse. Artificial intelligence and its subtypes, deep learning in particular, tend nowadays to have an expanding role in all fields of medicine, and diagnosing colon cancer is no exception. This report aims to summarize the entire application spectrum of deep learning in all diagnostic tests regarding colon cancer, from endoscopy and histologic examination to medical imaging and screening serologic tests.
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Affiliation(s)
- Dimitrios Bousis
- Department of Internal Medicine, General University Hospital of Patras, Patras, Greece
| | | | | | - Andreas Antzoulas
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | | | | | - Dimitrios Kehagias
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | | | - Konstantinos Kotis
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
| | | | - Francesk Mulita
- Department of Surgery, General University Hospital of Patras, Patras, Greece
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31
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Bakasa W, Viriri S. VGG16 Feature Extractor with Extreme Gradient Boost Classifier for Pancreas Cancer Prediction. J Imaging 2023; 9:138. [PMID: 37504815 PMCID: PMC10381878 DOI: 10.3390/jimaging9070138] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/19/2023] [Accepted: 07/04/2023] [Indexed: 07/29/2023] Open
Abstract
The prognosis of patients with pancreatic ductal adenocarcinoma (PDAC) is greatly improved by an early and accurate diagnosis. Several studies have created automated methods to forecast PDAC development utilising various medical imaging modalities. These papers give a general overview of the classification, segmentation, or grading of many cancer types utilising conventional machine learning techniques and hand-engineered characteristics, including pancreatic cancer. This study uses cutting-edge deep learning techniques to identify PDAC utilising computerised tomography (CT) medical imaging modalities. This work suggests that the hybrid model VGG16-XGBoost (VGG16-backbone feature extractor and Extreme Gradient Boosting-classifier) for PDAC images. According to studies, the proposed hybrid model performs better, obtaining an accuracy of 0.97 and a weighted F1 score of 0.97 for the dataset under study. The experimental validation of the VGG16-XGBoost model uses the Cancer Imaging Archive (TCIA) public access dataset, which has pancreas CT images. The results of this study can be extremely helpful for PDAC diagnosis from computerised tomography (CT) pancreas images, categorising them into five different tumours (T), node (N), and metastases (M) (TNM) staging system class labels, which are T0, T1, T2, T3, and T4.
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Affiliation(s)
- Wilson Bakasa
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 4041, South Africa
| | - Serestina Viriri
- School of Mathematics, Statistics and Computer Science, University of KwaZulu-Natal, Durban 4041, South Africa
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32
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Heidari A, Javaheri D, Toumaj S, Navimipour NJ, Rezaei M, Unal M. A new lung cancer detection method based on the chest CT images using Federated Learning and blockchain systems. Artif Intell Med 2023; 141:102572. [PMID: 37295902 DOI: 10.1016/j.artmed.2023.102572] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 03/16/2023] [Accepted: 04/27/2023] [Indexed: 06/12/2023]
Abstract
With an estimated five million fatal cases each year, lung cancer is one of the significant causes of death worldwide. Lung diseases can be diagnosed with a Computed Tomography (CT) scan. The scarcity and trustworthiness of human eyes is the fundamental issue in diagnosing lung cancer patients. The main goal of this study is to detect malignant lung nodules in a CT scan of the lungs and categorize lung cancer according to severity. In this work, cutting-edge Deep Learning (DL) algorithms were used to detect the location of cancerous nodules. Also, the real-life issue is sharing data with hospitals around the world while bearing in mind the organizations' privacy issues. Besides, the main problems for training a global DL model are creating a collaborative model and maintaining privacy. This study presented an approach that takes a modest amount of data from multiple hospitals and uses blockchain-based Federated Learning (FL) to train a global DL model. The data were authenticated using blockchain technology, and FL trained the model internationally while maintaining the organization's anonymity. First, we presented a data normalization approach that addresses the variability of data obtained from various institutions using various CT scanners. Furthermore, using a CapsNets method, we classified lung cancer patients in local mode. Finally, we devised a way to train a global model cooperatively utilizing blockchain technology and FL while maintaining anonymity. We also gathered data from real-life lung cancer patients for testing purposes. The suggested method was trained and tested on the Cancer Imaging Archive (CIA) dataset, Kaggle Data Science Bowl (KDSB), LUNA 16, and the local dataset. Finally, we performed extensive experiments with Python and its well-known libraries, such as Scikit-Learn and TensorFlow, to evaluate the suggested method. The findings showed that the method effectively detects lung cancer patients. The technique delivered 99.69 % accuracy with the smallest possible categorization error.
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Affiliation(s)
- Arash Heidari
- Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran
| | - Danial Javaheri
- Department of Computer Engineering, Chosun University, Gwangju 61452, Republic of Korea
| | - Shiva Toumaj
- Urmia University of Medical Sciences, Urmia, Iran
| | - Nima Jafari Navimipour
- Department of Computer Engineering, Kadir Has University, Istanbul, Turkiye; Future Technology Research Center, National Yunlin University of Science and Technology, Douliou, Yunlin 64002, Taiwan.
| | - Mahsa Rezaei
- Tabriz University of Medical Sciences, Faculty of Surgery, Tabriz, Iran
| | - Mehmet Unal
- Department of Computer Engineering, Nisantasi University, Istanbul, Turkiye
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33
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Khazaee Fadafen M, Rezaee K. Ensemble-based multi-tissue classification approach of colorectal cancer histology images using a novel hybrid deep learning framework. Sci Rep 2023; 13:8823. [PMID: 37258631 DOI: 10.1038/s41598-023-35431-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 05/17/2023] [Indexed: 06/02/2023] Open
Abstract
Colorectal cancer (CRC) is the second leading cause of cancer death in the world, so digital pathology is essential for assessing prognosis. Due to the increasing resolution and quantity of whole slide images (WSIs), as well as the lack of annotated information, previous methodologies cannot be generalized as effective decision-making systems. Since deep learning (DL) methods can handle large-scale applications, they can provide a viable alternative to histopathology image (HI) analysis. DL architectures, however, may not be sufficient to classify CRC tissues based on anatomical histopathology data. A dilated ResNet (dResNet) structure and attention module are used to generate deep feature maps in order to classify multiple tissues in HIs. In addition, neighborhood component analysis (NCA) overcomes the constraint of computational complexity. Data is fed into a deep support vector machine (SVM) based on an ensemble learning algorithm called DeepSVM after the features have been selected. CRC-5000 and NCT-CRC-HE-100 K datasets were analyzed to validate and test the hybrid procedure. We demonstrate that the hybrid model achieves 98.75% and 99.76% accuracy on CRC datasets. The results showed that only pathologists' labels could successfully classify unseen WSIs. Furthermore, the hybrid deep learning method outperforms state-of-the-art approaches in terms of computational efficiency and time. Using the proposed mechanism for tissue analysis, it will be possible to correctly predict CRC based on accurate pathology image classification.
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Affiliation(s)
- Masoud Khazaee Fadafen
- Department of Electrical Engineering, Technical and Vocational University (TVU), Tehran, Iran
| | - Khosro Rezaee
- Department of Biomedical Engineering, Meybod University, Meybod, Iran.
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Mohamed AAA, Hançerlioğullari A, Rahebi J, Ray MK, Roy S. Colon Disease Diagnosis with Convolutional Neural Network and Grasshopper Optimization Algorithm. Diagnostics (Basel) 2023; 13:diagnostics13101728. [PMID: 37238212 DOI: 10.3390/diagnostics13101728] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 05/10/2023] [Accepted: 05/11/2023] [Indexed: 05/28/2023] Open
Abstract
This paper presents a robust colon cancer diagnosis method based on the feature selection method. The proposed method for colon disease diagnosis can be divided into three steps. In the first step, the images' features were extracted based on the convolutional neural network. Squeezenet, Resnet-50, AlexNet, and GoogleNet were used for the convolutional neural network. The extracted features are huge, and the number of features cannot be appropriate for training the system. For this reason, the metaheuristic method is used in the second step to reduce the number of features. This research uses the grasshopper optimization algorithm to select the best features from the feature data. Finally, using machine learning methods, colon disease diagnosis was found to be accurate and successful. Two classification methods are applied for the evaluation of the proposed method. These methods include the decision tree and the support vector machine. The sensitivity, specificity, accuracy, and F1Score have been used to evaluate the proposed method. For Squeezenet based on the support vector machine, we obtained results of 99.34%, 99.41%, 99.12%, 98.91% and 98.94% for sensitivity, specificity, accuracy, precision, and F1Score, respectively. In the end, we compared the suggested recognition method's performance to the performances of other methods, including 9-layer CNN, random forest, 7-layer CNN, and DropBlock. We demonstrated that our solution outperformed the others.
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Affiliation(s)
- Amna Ali A Mohamed
- Department of Material Science and Engineering, University of Kastamonu, Kastamonu 37150, Turkey
| | | | - Javad Rahebi
- Department of Software Engineering, Istanbul Topkapi University, Istanbul 34087, Turkey
| | - Mayukh K Ray
- Department of Physics, Amity Institute of Applied Sciences, Amity University, Kolkata 700135, India
| | - Sudipta Roy
- Artificial Intelligence & Data Science, Jio Institute, Navi Mumbai 410206, India
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Tummala S, Kadry S, Nadeem A, Rauf HT, Gul N. An Explainable Classification Method Based on Complex Scaling in Histopathology Images for Lung and Colon Cancer. Diagnostics (Basel) 2023; 13:diagnostics13091594. [PMID: 37174985 PMCID: PMC10178684 DOI: 10.3390/diagnostics13091594] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 04/15/2023] [Accepted: 04/28/2023] [Indexed: 05/15/2023] Open
Abstract
Lung and colon cancers are among the leading causes of human mortality and morbidity. Early diagnostic work up of these diseases include radiography, ultrasound, magnetic resonance imaging, and computed tomography. Certain blood tumor markers for carcinoma lung and colon also aid in the diagnosis. Despite the lab and diagnostic imaging, histopathology remains the gold standard, which provides cell-level images of tissue under examination. To read these images, a histopathologist spends a large amount of time. Furthermore, using conventional diagnostic methods involve high-end equipment as well. This leads to limited number of patients getting final diagnosis and early treatment. In addition, there are chances of inter-observer errors. In recent years, deep learning has shown promising results in the medical field. This has helped in early diagnosis and treatment according to severity of disease. With the help of EffcientNetV2 models that have been cross-validated and tested fivefold, we propose an automated method for detecting lung (lung adenocarcinoma, lung benign, and lung squamous cell carcinoma) and colon (colon adenocarcinoma and colon benign) cancer subtypes from LC25000 histopathology images. A state-of-the-art deep learning architecture based on the principles of compound scaling and progressive learning, EffcientNetV2 large, medium, and small models. An accuracy of 99.97%, AUC of 99.99%, F1-score of 99.97%, balanced accuracy of 99.97%, and Matthew's correlation coefficient of 99.96% were obtained on the test set using the EffcientNetV2-L model for the 5-class classification of lung and colon cancers, outperforming the existing methods. Using gradCAM, we created visual saliency maps to precisely locate the vital regions in the histopathology images from the test set where the models put more attention during cancer subtype predictions. This visual saliency maps may potentially assist pathologists to design better treatment strategies. Therefore, it is possible to use the proposed pipeline in clinical settings for fully automated lung and colon cancer detection from histopathology images with explainability.
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Affiliation(s)
- Sudhakar Tummala
- Department of Electronics and Communication Engineering, School of Engineering and Sciences, SRM University-AP, Amaravati 522240, Andhra Pradesh, India
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman 346, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 36, Lebanon
| | - Ahmed Nadeem
- Department of Pharmacology & Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Hafiz Tayyab Rauf
- Centre for Smart Systems, AI and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
| | - Nadia Gul
- Wah Medical College affiliated with POF Hospital, Wah Cantt 47040, Pakistan
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Mokoatle M, Marivate V, Mapiye D, Bornman R, Hayes VM. A review and comparative study of cancer detection using machine learning: SBERT and SimCSE application. BMC Bioinformatics 2023; 24:112. [PMID: 36959534 PMCID: PMC10037872 DOI: 10.1186/s12859-023-05235-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 03/17/2023] [Indexed: 03/25/2023] Open
Abstract
BACKGROUND Using visual, biological, and electronic health records data as the sole input source, pretrained convolutional neural networks and conventional machine learning methods have been heavily employed for the identification of various malignancies. Initially, a series of preprocessing steps and image segmentation steps are performed to extract region of interest features from noisy features. Then, the extracted features are applied to several machine learning and deep learning methods for the detection of cancer. METHODS In this work, a review of all the methods that have been applied to develop machine learning algorithms that detect cancer is provided. With more than 100 types of cancer, this study only examines research on the four most common and prevalent cancers worldwide: lung, breast, prostate, and colorectal cancer. Next, by using state-of-the-art sentence transformers namely: SBERT (2019) and the unsupervised SimCSE (2021), this study proposes a new methodology for detecting cancer. This method requires raw DNA sequences of matched tumor/normal pair as the only input. The learnt DNA representations retrieved from SBERT and SimCSE will then be sent to machine learning algorithms (XGBoost, Random Forest, LightGBM, and CNNs) for classification. As far as we are aware, SBERT and SimCSE transformers have not been applied to represent DNA sequences in cancer detection settings. RESULTS The XGBoost model, which had the highest overall accuracy of 73 ± 0.13 % using SBERT embeddings and 75 ± 0.12 % using SimCSE embeddings, was the best performing classifier. In light of these findings, it can be concluded that incorporating sentence representations from SimCSE's sentence transformer only marginally improved the performance of machine learning models.
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Affiliation(s)
- Mpho Mokoatle
- Department of Computer Science, University of Pretoria, Pretoria, South Africa.
| | - Vukosi Marivate
- Department of Computer Science, University of Pretoria, Pretoria, South Africa
| | | | - Riana Bornman
- School of Health Systems and Public Health, University of Pretoria, Pretoria, South Africa
| | - Vanessa M Hayes
- School of Medical Sciences, The University of Sydney, Sydney, Australia
- School of Health Systems and Public Health, University of Pretoria, Pretoria, South Africa
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Al-Jabbar M, Alshahrani M, Senan EM, Ahmed IA. Histopathological Analysis for Detecting Lung and Colon Cancer Malignancies Using Hybrid Systems with Fused Features. Bioengineering (Basel) 2023; 10:bioengineering10030383. [PMID: 36978774 PMCID: PMC10045080 DOI: 10.3390/bioengineering10030383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 03/05/2023] [Accepted: 03/16/2023] [Indexed: 03/30/2023] Open
Abstract
Lung and colon cancer are among humanity's most common and deadly cancers. In 2020, there were 4.19 million people diagnosed with lung and colon cancer, and more than 2.7 million died worldwide. Some people develop lung and colon cancer simultaneously due to smoking which causes lung cancer, leading to an abnormal diet, which also causes colon cancer. There are many techniques for diagnosing lung and colon cancer, most notably the biopsy technique and its analysis in laboratories. Due to the scarcity of health centers and medical staff, especially in developing countries. Moreover, manual diagnosis takes a long time and is subject to differing opinions of doctors. Thus, artificial intelligence techniques solve these challenges. In this study, three strategies were developed, each with two systems for early diagnosis of histological images of the LC25000 dataset. Histological images have been improved, and the contrast of affected areas has been increased. The GoogLeNet and VGG-19 models of all systems produced high dimensional features, so redundant and unnecessary features were removed to reduce high dimensionality and retain essential features by the PCA method. The first strategy for diagnosing the histological images of the LC25000 dataset by ANN uses crucial features of GoogLeNet and VGG-19 models separately. The second strategy uses ANN with the combined features of GoogLeNet and VGG-19. One system reduced dimensions and combined, while the other combined high features and then reduced high dimensions. The third strategy uses ANN with fusion features of CNN models (GoogLeNet and VGG-19) and handcrafted features. With the fusion features of VGG-19 and handcrafted features, the ANN reached a sensitivity of 99.85%, a precision of 100%, an accuracy of 99.64%, a specificity of 100%, and an AUC of 99.86%.
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Affiliation(s)
- Mohammed Al-Jabbar
- Computer Department, Applied College, Najran University, Najran 66462, Saudi Arabia
| | - Mohammed Alshahrani
- Computer Department, Applied College, Najran University, Najran 66462, Saudi Arabia
| | - Ebrahim Mohammed Senan
- Department of Artificial Intelligence, Faculty of Computer Science and Information Technology, Alrazi University, Sana'a, Yemen
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Bostanci E, Kocak E, Unal M, Guzel MS, Acici K, Asuroglu T. Machine Learning Analysis of RNA-seq Data for Diagnostic and Prognostic Prediction of Colon Cancer. SENSORS (BASEL, SWITZERLAND) 2023; 23:3080. [PMID: 36991790 PMCID: PMC10052105 DOI: 10.3390/s23063080] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/09/2023] [Accepted: 03/11/2023] [Indexed: 06/19/2023]
Abstract
Data from omics studies have been used for prediction and classification of various diseases in biomedical and bioinformatics research. In recent years, Machine Learning (ML) algorithms have been used in many different fields related to healthcare systems, especially for disease prediction and classification tasks. Integration of molecular omics data with ML algorithms has offered a great opportunity to evaluate clinical data. RNA sequence (RNA-seq) analysis has been emerged as the gold standard for transcriptomics analysis. Currently, it is being used widely in clinical research. In our present work, RNA-seq data of extracellular vesicles (EV) from healthy and colon cancer patients are analyzed. Our aim is to develop models for prediction and classification of colon cancer stages. Five different canonical ML and Deep Learning (DL) classifiers are used to predict colon cancer of an individual with processed RNA-seq data. The classes of data are formed on the basis of both colon cancer stages and cancer presence (healthy or cancer). The canonical ML classifiers, which are k-Nearest Neighbor (kNN), Logistic Model Tree (LMT), Random Tree (RT), Random Committee (RC), and Random Forest (RF), are tested with both forms of the data. In addition, to compare the performance with canonical ML models, One-Dimensional Convolutional Neural Network (1-D CNN), Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM) DL models are utilized. Hyper-parameter optimizations of DL models are constructed by using genetic meta-heuristic optimization algorithm (GA). The best accuracy in cancer prediction is obtained with RC, LMT, and RF canonical ML algorithms as 97.33%. However, RT and kNN show 95.33% performance. The best accuracy in cancer stage classification is achieved with RF as 97.33%. This result is followed by LMT, RC, kNN, and RT with 96.33%, 96%, 94.66%, and 94%, respectively. According to the results of the experiments with DL algorithms, the best accuracy in cancer prediction is obtained with 1-D CNN as 97.67%. BiLSTM and LSTM show 94.33% and 93.67% performance, respectively. In classification of the cancer stages, the best accuracy is achieved with BiLSTM as 98%. 1-D CNN and LSTM show 97% and 94.33% performance, respectively. The results reveal that both canonical ML and DL models may outperform each other for different numbers of features.
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Affiliation(s)
- Erkan Bostanci
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Engin Kocak
- Department of Analytical Chemistry, Faculty of Gülhane Pharmacy, University of Health Sciences, 06018 Ankara, Turkey
| | - Metehan Unal
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Mehmet Serdar Guzel
- Department of Computer Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Koray Acici
- Department of Artificial Intelligence and Data Engineering, Faculty of Engineering, Ankara University, 06830 Ankara, Turkey
| | - Tunc Asuroglu
- Faculty of Medicine and Health Technology, Tampere University, 33720 Tampere, Finland
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Leveraging Marine Predators Algorithm with Deep Learning for Lung and Colon Cancer Diagnosis. Cancers (Basel) 2023; 15:cancers15051591. [PMID: 36900381 PMCID: PMC10001330 DOI: 10.3390/cancers15051591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 02/04/2023] [Accepted: 02/08/2023] [Indexed: 03/08/2023] Open
Abstract
Cancer is a deadly disease caused by various biochemical abnormalities and genetic diseases. Colon and lung cancer have developed as two major causes of disability and death in human beings. The histopathological detection of these malignancies is a vital element in determining the optimal solution. Timely and initial diagnosis of the sickness on either front diminishes the possibility of death. Deep learning (DL) and machine learning (ML) methods are used to hasten such cancer recognition, allowing the research community to examine more patients in a much shorter period and at a less cost. This study introduces a marine predator's algorithm with deep learning as a lung and colon cancer classification (MPADL-LC3) technique. The presented MPADL-LC3 technique aims to properly discriminate different types of lung and colon cancer on histopathological images. To accomplish this, the MPADL-LC3 technique employs CLAHE-based contrast enhancement as a pre-processing step. In addition, the MPADL-LC3 technique applies MobileNet to derive feature vector generation. Meanwhile, the MPADL-LC3 technique employs MPA as a hyperparameter optimizer. Furthermore, deep belief networks (DBN) can be applied for lung and color classification. The simulation values of the MPADL-LC3 technique were examined on benchmark datasets. The comparison study highlighted the enhanced outcomes of the MPADL-LC3 system in terms of different measures.
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40
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Appadurai JP, G S, Prabhu Kavin B, C K, Lai WC. Multi-Process Remora Enhanced Hyperparameters of Convolutional Neural Network for Lung Cancer Prediction. Biomedicines 2023; 11:biomedicines11030679. [PMID: 36979657 PMCID: PMC10045623 DOI: 10.3390/biomedicines11030679] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2022] [Revised: 01/30/2023] [Accepted: 02/08/2023] [Indexed: 03/30/2023] Open
Abstract
In recent years, lung cancer prediction is an essential topic for reducing the death rate of humans. In the literature section, some papers are reviewed that reduce the accuracy level during the prediction stage. Hence, in this paper, we develop a Multi-Process Remora Optimized Hyperparameters of Convolutional Neural Network (MPROH-CNN) aimed at lung cancer prediction. The proposed technique can be utilized to detect the CT images of the human lung. The proposed technique proceeds with four phases, including pre-processing, feature extraction and classification. Initially, the databases are collected from the open-source system. After that, the collected CT images contain unwanted noise, which affects classification efficiency. So, the pre-processing techniques can be considered to remove unwanted noise from the input images, such as filtering and contrast enhancement. Following that, the essential features are extracted with the assistance of feature extraction techniques such as histogram, texture and wavelet. The extracted features are utilized to classification stage. The proposed classifier is a combination of the Remora Optimization Algorithm (ROA) and Convolutional Neural Network (CNN). In the CNN, the ROA is utilized for multi process optimization such as structure optimization and hyperparameter optimization. The proposed methodology is implemented in MATLAB and performances are evaluated by utilized performance matrices such as accuracy, precision, recall, specificity, sensitivity and F_Measure. To validate the projected approach, it is compared with the traditional techniques CNN, CNN-Particle Swarm Optimization (PSO) and CNN-Firefly Algorithm (FA), respectively. From the analysis, the proposed method achieved a 0.98 accuracy level in the lung cancer prediction.
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Affiliation(s)
- Jothi Prabha Appadurai
- Computer Science and Engineering Department, Kakatiya Institute of Technology and Science, Warangal 506015, Telangana, India
| | - Suganeshwari G
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai 600127, Tamil Nadu, India
| | - Balasubramanian Prabhu Kavin
- Department of Data Science and Business Systems, College of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Chengalpattu District, Chennai 603203, Tamil Nadu, India
| | - Kavitha C
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai 600119, Tamil Nadu, India
| | - Wen-Cheng Lai
- Bachelor Program in Industrial Projects, National Yunlin University of Science and Technology, Douliu 640301, Taiwan
- Department Electronic Engineering, National Yunlin University of Science and Technology, Douliu 640301, Taiwan
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41
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Cifci MA. A Deep Learning-Based Framework for Uncertainty Quantification in Medical Imaging Using the DropWeak Technique: An Empirical Study with Baresnet. Diagnostics (Basel) 2023; 13:800. [PMID: 36832288 PMCID: PMC9955446 DOI: 10.3390/diagnostics13040800] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Revised: 02/13/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023] Open
Abstract
Lung cancer is a leading cause of cancer-related deaths globally. Early detection is crucial for improving patient survival rates. Deep learning (DL) has shown promise in the medical field, but its accuracy must be evaluated, particularly in the context of lung cancer classification. In this study, we conducted uncertainty analysis on various frequently used DL architectures, including Baresnet, to assess the uncertainties in the classification results. This study focuses on the use of deep learning for the classification of lung cancer, which is a critical aspect of improving patient survival rates. The study evaluates the accuracy of various deep learning architectures, including Baresnet, and incorporates uncertainty quantification to assess the level of uncertainty in the classification results. The study presents a novel automatic tumor classification system for lung cancer based on CT images, which achieves a classification accuracy of 97.19% with an uncertainty quantification. The results demonstrate the potential of deep learning in lung cancer classification and highlight the importance of uncertainty quantification in improving the accuracy of classification results. This study's novelty lies in the incorporation of uncertainty quantification in deep learning for lung cancer classification, which can lead to more reliable and accurate diagnoses in clinical settings.
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Affiliation(s)
- Mehmet Akif Cifci
- The Institute of Computer Technology, Tu Wien University, 1040 Vienna, Austria;
- Department of Computer Eng., Bandirma Onyedi Eylul University, 10200 Balikesir, Turkey
- Department of Informatics, Klaipeda University, 92294 Klaipeda, Lithuania;
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Robustness Fine-Tuning Deep Learning Model for Cancers Diagnosis Based on Histopathology Image Analysis. Diagnostics (Basel) 2023; 13:diagnostics13040699. [PMID: 36832186 PMCID: PMC9955143 DOI: 10.3390/diagnostics13040699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/01/2023] [Accepted: 02/10/2023] [Indexed: 02/16/2023] Open
Abstract
Histopathology is the most accurate way to diagnose cancer and identify prognostic and therapeutic targets. The likelihood of survival is significantly increased by early cancer detection. With deep networks' enormous success, significant attempts have been made to analyze cancer disorders, particularly colon and lung cancers. In order to do this, this paper examines how well deep networks can diagnose various cancers using histopathology image processing. This work intends to increase the performance of deep learning architecture in processing histopathology images by constructing a novel fine-tuning deep network for colon and lung cancers. Such adjustments are performed using regularization, batch normalization, and hyperparameters optimization. The suggested fine-tuned model was evaluated using the LC2500 dataset. Our proposed model's average precision, recall, F1-score, specificity, and accuracy were 99.84%, 99.85%, 99.84%, 99.96%, and 99.94%, respectively. The experimental findings reveal that the suggested fine-tuned learning model based on the pre-trained ResNet101 network achieves higher results against recent state-of-the-art approaches and other current powerful CNN models.
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43
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Using Deep Learning with Bayesian–Gaussian Inspired Convolutional Neural Architectural Search for Cancer Recognition and Classification from Histopathological Image Frames. JOURNAL OF HEALTHCARE ENGINEERING 2023. [DOI: 10.1155/2023/4597445] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
We propose a neural architectural search model which examines histopathological images to detect the presence of cancer in both lung and colon tissues. In recent times, deep artificial neural networks have made tremendous impacts in healthcare. However, obtaining an optimal artificial neural network model that could yield excellent performance during training, evaluation, and inferencing has been a bottleneck for researchers. Our method uses a Bayesian convolutional neural architectural search algorithm in collaboration with Gaussian processes to provide an efficient neural network architecture for efficient colon and lung cancer classification and recognition. The proposed model learns by using the Gaussian process to estimate the required optimal architectural values by choosing a set of model parameters through the exploitation of the expected improvement (EI) values, thereby minimizing the number of sampled trials and suggesting the best model architecture. Several experiments were conducted, and a landmark performance was obtained in both validation and test data through the evaluation of the proposed model on a dataset consisting of 25,000 images of five different classes with convergence and F1-score matrices.
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Lalitha R, Krishna Prasad P, Rama Reddy T, Kavitha K, Srinivas R, Ravi Kiran B. Efficient adaptive enhanced adaboost based detection of spinal abnormalities by Machine learning approaches. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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45
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Tharwat M, Sakr NA, El-Sappagh S, Soliman H, Kwak KS, Elmogy M. Colon Cancer Diagnosis Based on Machine Learning and Deep Learning: Modalities and Analysis Techniques. SENSORS (BASEL, SWITZERLAND) 2022; 22:9250. [PMID: 36501951 PMCID: PMC9739266 DOI: 10.3390/s22239250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/24/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
The treatment and diagnosis of colon cancer are considered to be social and economic challenges due to the high mortality rates. Every year, around the world, almost half a million people contract cancer, including colon cancer. Determining the grade of colon cancer mainly depends on analyzing the gland's structure by tissue region, which has led to the existence of various tests for screening that can be utilized to investigate polyp images and colorectal cancer. This article presents a comprehensive survey on the diagnosis of colon cancer. This covers many aspects related to colon cancer, such as its symptoms and grades as well as the available imaging modalities (particularly, histopathology images used for analysis) in addition to common diagnosis systems. Furthermore, the most widely used datasets and performance evaluation metrics are discussed. We provide a comprehensive review of the current studies on colon cancer, classified into deep-learning (DL) and machine-learning (ML) techniques, and we identify their main strengths and limitations. These techniques provide extensive support for identifying the early stages of cancer that lead to early treatment of the disease and produce a lower mortality rate compared with the rate produced after symptoms develop. In addition, these methods can help to prevent colorectal cancer from progressing through the removal of pre-malignant polyps, which can be achieved using screening tests to make the disease easier to diagnose. Finally, the existing challenges and future research directions that open the way for future work in this field are presented.
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Affiliation(s)
- Mai Tharwat
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
| | - Nehal A. Sakr
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
| | - Shaker El-Sappagh
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Benha University, Benha 13512, Egypt
- Faculty of Computer Science and Engineering, Galala University, Suez 435611, Egypt
| | - Hassan Soliman
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
| | - Kyung-Sup Kwak
- Department of Information and Communication Engineering, Inha University, Incheon 22212, Republic of Korea
| | - Mohammed Elmogy
- Information Technology Department, Faculty of Computers and Information, Mansoura University, Mansoura 35516, Egypt
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Attallah O, Aslan MF, Sabanci K. A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods. Diagnostics (Basel) 2022; 12:diagnostics12122926. [PMID: 36552933 PMCID: PMC9776637 DOI: 10.3390/diagnostics12122926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 11/19/2022] [Accepted: 11/22/2022] [Indexed: 11/25/2022] Open
Abstract
Among the leading causes of mortality and morbidity in people are lung and colon cancers. They may develop concurrently in organs and negatively impact human life. If cancer is not diagnosed in its early stages, there is a great likelihood that it will spread to the two organs. The histopathological detection of such malignancies is one of the most crucial components of effective treatment. Although the process is lengthy and complex, deep learning (DL) techniques have made it feasible to complete it more quickly and accurately, enabling researchers to study a lot more patients in a short time period and for a lot less cost. Earlier studies relied on DL models that require great computational ability and resources. Most of them depended on individual DL models to extract features of high dimension or to perform diagnoses. However, in this study, a framework based on multiple lightweight DL models is proposed for the early detection of lung and colon cancers. The framework utilizes several transformation methods that perform feature reduction and provide a better representation of the data. In this context, histopathology scans are fed into the ShuffleNet, MobileNet, and SqueezeNet models. The number of deep features acquired from these models is subsequently reduced using principal component analysis (PCA) and fast Walsh-Hadamard transform (FHWT) techniques. Following that, discrete wavelet transform (DWT) is used to fuse the FWHT's reduced features obtained from the three DL models. Additionally, the three DL models' PCA features are concatenated. Finally, the diminished features as a result of PCA and FHWT-DWT reduction and fusion processes are fed to four distinct machine learning algorithms, reaching the highest accuracy of 99.6%. The results obtained using the proposed framework based on lightweight DL models show that it can distinguish lung and colon cancer variants with a lower number of features and less computational complexity compared to existing methods. They also prove that utilizing transformation methods to reduce features can offer a superior interpretation of the data, thus improving the diagnosis procedure.
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Affiliation(s)
- Omneya Attallah
- Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt
- Correspondence:
| | - Muhammet Fatih Aslan
- Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, 70100 Karaman, Turkey
| | - Kadir Sabanci
- Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, 70100 Karaman, Turkey
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CJT-DEO: Condorcet’s Jury Theorem and Differential Evolution Optimization based ensemble of deep neural networks for pulmonary and colorectal cancer classification. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109872] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Dabass M, Vashisth S, Vig R. MTU: A multi-tasking U-net with hybrid convolutional learning and attention modules for cancer classification and gland Segmentation in Colon Histopathological Images. Comput Biol Med 2022; 150:106095. [PMID: 36179516 DOI: 10.1016/j.compbiomed.2022.106095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 08/31/2022] [Accepted: 09/10/2022] [Indexed: 11/17/2022]
Abstract
A clinically comparable multi-tasking computerized deep U-Net-based model is demonstrated in this paper. It intends to offer clinical gland morphometric information and cancer grade classification to be provided as referential opinions for pathologists in order to abate human errors. It embraces enhanced feature learning capability that aids in extraction of potent multi-scale features; efficacious semantic gap recovery during feature concatenation; and successful interception of resolution-degradation and vanishing gradient problems while performing moderate computations. It is proposed by integrating three unique novel structural components namely Hybrid Convolutional Learning Units in the encoder and decoder, Attention Learning Units in skip connection, and Multi-Scalar Dilated Transitional Unit as the transitional layer in the traditional U-Net architecture. These units are composed of the amalgamated phenomenon of multi-level convolutional learning through conventional, atrous, residual, depth-wise, and point-wise convolutions which are further incorporated with target-specific attention learning and enlarged effectual receptive field size. Also, pre-processing techniques of patch-sampling, augmentation (color and morphological), stain-normalization, etc. are employed to burgeon its generalizability. To build network invariance towards digital variability, exhaustive experiments are conducted using three public datasets (Colorectal Adenocarcinoma Gland (CRAG), Gland Segmentation (GlaS) challenge, and Lung Colon-25000 (LC-25K) dataset)) and then its robustness is verified using an in-house private dataset of Hospital Colon (HosC). For the cancer classification, the proposed model achieved results of Accuracy (CRAG(95%), GlaS(97.5%), LC-25K(99.97%), HosC(99.45%)), Precision (CRAG(0.9678), GlaS(0.9768), LC-25K(1), HosC(1)), F1-score (CRAG(0.968), GlaS(0.977), LC 25K(0.9997), HosC(0.9965)), and Recall (CRAG(0.9677), GlaS(0.9767), LC-25K(0.9994), HosC(0.9931)). For the gland detection and segmentation, the proposed model achieved competitive results of F1-score (CRAG(0.924), GlaS(Test A(0.949), Test B(0.918)), LC-25K(0.916), HosC(0.959)); Object-Dice Index (CRAG(0.959), GlaS(Test A(0.956), Test B(0.909)), LC-25K(0.929), HosC(0.922)), and Object-Hausdorff Distance (CRAG(90.47), GlaS(Test A(23.17), Test B(71.53)), LC-25K(96.28), HosC(85.45)). In addition, the activation mappings for testing the interpretability of the classification decision-making process are reported by utilizing techniques of Local Interpretable Model-Agnostic Explanations, Occlusion Sensitivity, and Gradient-Weighted Class Activation Mappings. This is done to provide further evidence about the model's self-learning capability of the comparable patterns considered relevant by pathologists without any pre-requisite for annotations. These activation mapping visualization outcomes are evaluated by proficient pathologists, and they delivered these images with a class-path validation score of (CRAG(9.31), GlaS(9.25), LC-25K(9.05), and HosC(9.85)). Furthermore, the seg-path validation score of (GlaS (Test A(9.40), Test B(9.25)), CRAG(9.27), LC-25K(9.01), HosC(9.19)) given by multiple pathologists is included for the final segmented outcomes to substantiate the clinical relevance and suitability for facilitation at the clinical level. The proposed model will aid pathologists to formulate an accurate diagnosis by providing a referential opinion during the morphology assessment of histopathology images. It will reduce unintentional human error in cancer diagnosis and consequently will enhance patient survival rate.
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Affiliation(s)
- Manju Dabass
- EECE Deptt, The NorthCap University, Gurugram, 122017, India.
| | - Sharda Vashisth
- EECE Deptt, The NorthCap University, Gurugram, 122017, India
| | - Rekha Vig
- EECE Deptt, The NorthCap University, Gurugram, 122017, India
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Liu Y, Wang H, Song K, Sun M, Shao Y, Xue S, Li L, Li Y, Cai H, Jiao Y, Sun N, Liu M, Zhang T. CroReLU: Cross-Crossing Space-Based Visual Activation Function for Lung Cancer Pathology Image Recognition. Cancers (Basel) 2022; 14:5181. [PMID: 36358598 PMCID: PMC9657127 DOI: 10.3390/cancers14215181] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Revised: 10/14/2022] [Accepted: 10/19/2022] [Indexed: 08/13/2023] Open
Abstract
Lung cancer is one of the most common malignant tumors in human beings. It is highly fatal, as its early symptoms are not obvious. In clinical medicine, physicians rely on the information provided by pathology tests as an important reference for the final diagnosis of many diseases. Therefore, pathology diagnosis is known as the gold standard for disease diagnosis. However, the complexity of the information contained in pathology images and the increase in the number of patients far outpace the number of pathologists, especially for the treatment of lung cancer in less developed countries. To address this problem, we propose a plug-and-play visual activation function (AF), CroReLU, based on a priori knowledge of pathology, which makes it possible to use deep learning models for precision medicine. To the best of our knowledge, this work is the first to optimize deep learning models for pathology image diagnosis from the perspective of AFs. By adopting a unique crossover window design for the activation layer of the neural network, CroReLU is equipped with the ability to model spatial information and capture histological morphological features of lung cancer such as papillary, micropapillary, and tubular alveoli. To test the effectiveness of this design, 776 lung cancer pathology images were collected as experimental data. When CroReLU was inserted into the SeNet network (SeNet_CroReLU), the diagnostic accuracy reached 98.33%, which was significantly better than that of common neural network models at this stage. The generalization ability of the proposed method was validated on the LC25000 dataset with completely different data distribution and recognition tasks in the face of practical clinical needs. The experimental results show that CroReLU has the ability to recognize inter- and intra-class differences in cancer pathology images, and that the recognition accuracy exceeds the extant research work on the complex design of network layers.
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Affiliation(s)
- Yunpeng Liu
- Department of Thoracic Surgery, The First Hospital of Jilin University, Changchun 130012, China
| | - Haoran Wang
- School of Instrument and Electrical Engineering, Jilin University, Changchun 130012, China
| | - Kaiwen Song
- School of Instrument and Electrical Engineering, Jilin University, Changchun 130012, China
| | - Mingyang Sun
- School of Instrument and Electrical Engineering, Jilin University, Changchun 130012, China
| | - Yanbin Shao
- School of Instrument and Electrical Engineering, Jilin University, Changchun 130012, China
| | - Songfeng Xue
- School of Instrument and Electrical Engineering, Jilin University, Changchun 130012, China
| | - Liyuan Li
- School of Instrument and Electrical Engineering, Jilin University, Changchun 130012, China
| | - Yuguang Li
- School of Instrument and Electrical Engineering, Jilin University, Changchun 130012, China
| | - Hongqiao Cai
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital, Jilin University, 71 Xinmin Street, Changchun 130021, China
| | - Yan Jiao
- Department of Hepatobiliary and Pancreatic Surgery, The First Hospital, Jilin University, 71 Xinmin Street, Changchun 130021, China
| | - Nao Sun
- Center for Reproductive Medicine and Center for Prenatal Diagnosis, The First Hospital of Jilin University, Changchun 130012, China
| | - Mingyang Liu
- School of Instrument and Electrical Engineering, Jilin University, Changchun 130012, China
| | - Tianyu Zhang
- School of Instrument and Electrical Engineering, Jilin University, Changchun 130012, China
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Rashed BM, Popescu N. Critical Analysis of the Current Medical Image-Based Processing Techniques for Automatic Disease Evaluation: Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2022; 22:7065. [PMID: 36146414 PMCID: PMC9501859 DOI: 10.3390/s22187065] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2022] [Revised: 09/06/2022] [Accepted: 09/14/2022] [Indexed: 06/16/2023]
Abstract
Medical image processing and analysis techniques play a significant role in diagnosing diseases. Thus, during the last decade, several noteworthy improvements in medical diagnostics have been made based on medical image processing techniques. In this article, we reviewed articles published in the most important journals and conferences that used or proposed medical image analysis techniques to diagnose diseases. Starting from four scientific databases, we applied the PRISMA technique to efficiently process and refine articles until we obtained forty research articles published in the last five years (2017-2021) aimed at answering our research questions. The medical image processing and analysis approaches were identified, examined, and discussed, including preprocessing, segmentation, feature extraction, classification, evaluation metrics, and diagnosis techniques. This article also sheds light on machine learning and deep learning approaches. We also focused on the most important medical image processing techniques used in these articles to establish the best methodologies for future approaches, discussing the most efficient ones and proposing in this way a comprehensive reference source of methods of medical image processing and analysis that can be very useful in future medical diagnosis systems.
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