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Rehman A, Mahmood T, Alamri FS, Saba T, Naseem S. Advanced feature learning and classification of microscopic breast abnormalities using a robust deep transfer learning technique. Microsc Res Tech 2024; 87:1862-1888. [PMID: 38553901 DOI: 10.1002/jemt.24557] [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: 09/25/2023] [Revised: 01/21/2024] [Accepted: 03/14/2024] [Indexed: 07/03/2024]
Abstract
Breast cancer is a major health threat, with early detection crucial for improving cure and survival rates. Current systems rely on imaging technology, but digital pathology and computerized analysis can enhance accuracy, reduce false predictions, and improve medical care for breast cancer patients. The study explores the challenges in identifying benign and malignant breast cancer lesions using microscopic image datasets. It introduces a low-dimensional multiple-channel feature-based method for breast cancer microscopic image recognition, overcoming limitations in feature utilization and computational complexity. The method uses RGB channels for image processing and extracts features using level co-occurrence matrix, wavelet, Gabor, and histogram of oriented gradient. This approach aims to improve diagnostic efficiency and accuracy in breast cancer treatment. The core of our method is the SqE-DDConvNet algorithm, which utilizes a 3 × 1 convolution kernel, SqE-DenseNet module, bilinear interpolation, and global average pooling to enhance recognition accuracy and training efficiency. Additionally, we incorporate transfer learning with pre-trained models, including mVVGNet16, EfficientNetV2B3, ResNet101V2, and CN2XNet, preserving spatial information and achieving higher accuracy under varying magnification conditions. The method achieves higher accuracy compared to baseline models, including texture and deep semantic features. This deep learning-based methodology contributes to more accurate image classification and unique image recognition in breast cancer microscopic images. RESEARCH HIGHLIGHTS: Introduces a low-dimensional multiple-channel feature-based method for breast cancer microscopic image recognition. Uses RGB channels for image processing and extracts features using level co-occurrence matrix, wavelet, Gabor, and histogram of oriented gradient. Employs the SqE-DDConvNet algorithm for enhanced recognition accuracy and training efficiency. Transfer learning with pre-trained models preserves spatial information and achieves higher accuracy under varying magnification conditions. Evaluates predictive efficacy of transfer learning paradigms within microscopic analysis. Utilizes CNN-based pre-trained algorithms to enhance network performance.
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Affiliation(s)
- Amjad Rehman
- Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh, Kingdom of Saudi Arabia
| | - Tariq Mahmood
- Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh, Kingdom of Saudi Arabia
- Faculty of Information Sciences, University of Education, Vehari Campus, Vehari, Pakistan
| | - Faten S Alamri
- Department of Mathematical Sciences, College of Science, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Tanzila Saba
- Artificial Intelligence and Data Analytics (AIDA) Lab, CCIS Prince Sultan University, Riyadh, Kingdom of Saudi Arabia
| | - Shahid Naseem
- Faculty of Information Sciences, Division of Science and Technology, University of Education, Lahore, Pakistan
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2
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Thomas L, Sheeja MK. Fourier ptychographic and deep learning using breast cancer histopathological image classification. JOURNAL OF BIOPHOTONICS 2023; 16:e202300194. [PMID: 37296518 DOI: 10.1002/jbio.202300194] [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: 05/27/2023] [Revised: 06/07/2023] [Accepted: 06/08/2023] [Indexed: 06/12/2023]
Abstract
Automated, as well as accurate classification with breast cancer histological images, was crucial for medical applications because of detecting malignant tumors via histopathological images. In this work create a Fourier ptychographic (FP) and deep learning using breast cancer histopathological image classification. Here the FP method used in the process begins with such a random guess that builds a high-resolution complex hologram, subsequently uses iterative retrieval using FP constraints to stitch around each other low-resolution multi-view means of production owned from either the hologram's high-resolution hologram's elemental images captured via integral imaging. Next, the feature extraction process includes entropy, geometrical features, and textural features. The entropy-based normalization is used to optimize the features. Finally, it attains the classification process of the proposed ENDNN classifies the breast cancer images into normal or abnormal. The experimental outcomes demonstrate that our presented technique overtakes the traditional techniques.
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Affiliation(s)
- Leena Thomas
- Department of Electronics & Communication Engineering, Sree Chitra Thirunal College of Engineering, Thiruvananthapuram, Kerala, India
- APJ Abdul Kalam Technological University, Kerala, India
- College of Engineering Kallooppara, Pathanamthitta, Kerala, India
| | - M K Sheeja
- Department of Electronics & Communication Engineering, Sree Chitra Thirunal College of Engineering, Thiruvananthapuram, Kerala, India
- APJ Abdul Kalam Technological University, Kerala, India
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3
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Yin Z, Yao C, Zhang L, Qi S. Application of artificial intelligence in diagnosis and treatment of colorectal cancer: A novel Prospect. Front Med (Lausanne) 2023; 10:1128084. [PMID: 36968824 PMCID: PMC10030915 DOI: 10.3389/fmed.2023.1128084] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 02/13/2023] [Indexed: 03/29/2023] Open
Abstract
In the past few decades, according to the rapid development of information technology, artificial intelligence (AI) has also made significant progress in the medical field. Colorectal cancer (CRC) is the third most diagnosed cancer worldwide, and its incidence and mortality rates are increasing yearly, especially in developing countries. This article reviews the latest progress in AI in diagnosing and treating CRC based on a systematic collection of previous literature. Most CRCs transform from polyp mutations. The computer-aided detection systems can significantly improve the polyp and adenoma detection rate by early colonoscopy screening, thereby lowering the possibility of mutating into CRC. Machine learning and bioinformatics analysis can help screen and identify more CRC biomarkers to provide the basis for non-invasive screening. The Convolutional neural networks can assist in reading histopathologic tissue images, reducing the experience difference among doctors. Various studies have shown that AI-based high-level auxiliary diagnostic systems can significantly improve the readability of medical images and help clinicians make more accurate diagnostic and therapeutic decisions. Moreover, Robotic surgery systems such as da Vinci have been more and more commonly used to treat CRC patients, according to their precise operating performance. The application of AI in neoadjuvant chemoradiotherapy has further improved the treatment and efficacy evaluation of CRC. In addition, AI represented by deep learning in gene sequencing research offers a new treatment option. All of these things have seen that AI has a promising prospect in the era of precision medicine.
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Affiliation(s)
- Zugang Yin
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Chenhui Yao
- Department of General Surgery, The First Affiliated Hospital of Dalian Medical University, Dalian, China
- *Correspondence: Chenhui Yao,
| | - Limin Zhang
- Department of Respiratory, The First Affiliated Hospital of Dalian Medical University, Dalian, China
| | - Shaohua Qi
- Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
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4
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Zou Y, Chen S, Che C, Zhang J, Zhang Q. Breast cancer histopathology image classification based on dual-stream high-order network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.104007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
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5
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Automated histological classification for digital pathology images of colonoscopy specimen via deep learning. Sci Rep 2022; 12:12804. [PMID: 35896791 PMCID: PMC9329279 DOI: 10.1038/s41598-022-16885-x] [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: 02/15/2022] [Accepted: 07/18/2022] [Indexed: 11/09/2022] Open
Abstract
Colonoscopy is an effective tool to detect colorectal lesions and needs the support of pathological diagnosis. This study aimed to develop and validate deep learning models that automatically classify digital pathology images of colon lesions obtained from colonoscopy-related specimen. Histopathological slides of colonoscopic biopsy or resection specimens were collected and grouped into six classes by disease category: adenocarcinoma, tubular adenoma (TA), traditional serrated adenoma (TSA), sessile serrated adenoma (SSA), hyperplastic polyp (HP), and non-specific lesions. Digital photographs were taken of each pathological slide to fine-tune two pre-trained convolutional neural networks, and the model performances were evaluated. A total of 1865 images were included from 703 patients, of which 10% were used as a test dataset. For six-class classification, the mean diagnostic accuracy was 97.3% (95% confidence interval [CI], 96.0–98.6%) by DenseNet-161 and 95.9% (95% CI 94.1–97.7%) by EfficientNet-B7. The per-class area under the receiver operating characteristic curve (AUC) was highest for adenocarcinoma (1.000; 95% CI 0.999–1.000) by DenseNet-161 and TSA (1.000; 95% CI 1.000–1.000) by EfficientNet-B7. The lowest per-class AUCs were still excellent: 0.991 (95% CI 0.983–0.999) for HP by DenseNet-161 and 0.995 for SSA (95% CI 0.992–0.998) by EfficientNet-B7. Deep learning models achieved excellent performances for discriminating adenocarcinoma from non-adenocarcinoma lesions with an AUC of 0.995 or 0.998. The pathognomonic area for each class was appropriately highlighted in digital images by saliency map, particularly focusing epithelial lesions. Deep learning models might be a useful tool to help the diagnosis for pathologic slides of colonoscopy-related specimens.
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6
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Albashish D. Ensemble of adapted convolutional neural networks (CNN) methods for classifying colon histopathological images. PeerJ Comput Sci 2022; 8:e1031. [PMID: 35875641 PMCID: PMC9299234 DOI: 10.7717/peerj-cs.1031] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 06/17/2022] [Indexed: 06/15/2023]
Abstract
Deep convolutional neural networks (CNN) manifest the potential for computer-aided diagnosis systems (CADs) by learning features directly from images rather than using traditional feature extraction methods. Nevertheless, due to the limited sample sizes and heterogeneity in tumor presentation in medical images, CNN models suffer from training issues, including training from scratch, which leads to overfitting. Alternatively, a pre-trained neural network's transfer learning (TL) is used to derive tumor knowledge from medical image datasets using CNN that were designed for non-medical activations, alleviating the need for large datasets. This study proposes two ensemble learning techniques: E-CNN (product rule) and E-CNN (majority voting). These techniques are based on the adaptation of the pretrained CNN models to classify colon cancer histopathology images into various classes. In these ensembles, the individuals are, initially, constructed by adapting pretrained DenseNet121, MobileNetV2, InceptionV3, and VGG16 models. The adaptation of these models is based on a block-wise fine-tuning policy, in which a set of dense and dropout layers of these pretrained models is joined to explore the variation in the histology images. Then, the models' decisions are fused via product rule and majority voting aggregation methods. The proposed model was validated against the standard pretrained models and the most recent works on two publicly available benchmark colon histopathological image datasets: Stoean (357 images) and Kather colorectal histology (5,000 images). The results were 97.20% and 91.28% accurate, respectively. The achieved results outperformed the state-of-the-art studies and confirmed that the proposed E-CNNs could be extended to be used in various medical image applications.
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Affiliation(s)
- Dheeb Albashish
- Computer Science Department/ Prince Abdullah bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Alsalt, Jordan
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7
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Hou C, Li J, Wang W, Sun L, Zhang J. Second-order asymmetric convolution network for breast cancer histopathology image classification. JOURNAL OF BIOPHOTONICS 2022; 15:e202100370. [PMID: 35076187 DOI: 10.1002/jbio.202100370] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 01/20/2022] [Accepted: 01/24/2022] [Indexed: 06/14/2023]
Abstract
Recently, convolutional neural networks (CNNs) have been widely utilized for breast cancer histopathology image classification. Besides, research works have also convinced that deep high-order statistic models obviously outperform corresponding first-order counterparts in vision tasks. Inspired by this, we attempt to explore global deep high-order statistics to distinguish breast cancer histopathology images. To further boost the classification performance, we also integrate asymmetric convolution into the second-order network and propose a novel second-order asymmetric convolution network (SoACNet). SoACNet adopts a series of asymmetric convolution blocks to replace each stand square-kernel convolutional layer of the backbone architecture, followed by a global covariance pooling to compute second-order statistics of deep features, leading to a more robust representation of histopathology images. Extensive experiments on the public BreakHis dataset demonstrate the effectiveness of SoACNet for breast cancer histopathology image classification, which achieves competitive performance with the state-of-the-arts.
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Affiliation(s)
- Cunqiao Hou
- School of Computer Science and Engineering, Dalian Minzu University, Dalian, China
- Institute of Machine Intelligence and Bio-computing, Dalian Minzu University, Dalian, China
| | - Jiasen Li
- Information Technology Department, Bank of TianJin, Tianjin, China
- Key Lab of Advanced Design and Intelligent Computing (Ministry of Education), Dalian University, Dalian, China
| | - Wei Wang
- School of Computer Science and Engineering, Dalian Minzu University, Dalian, China
| | - Lin Sun
- Information Center, Beijing Tongren Hospital, Beijing, China
| | - Jianxin Zhang
- School of Computer Science and Engineering, Dalian Minzu University, Dalian, China
- Institute of Machine Intelligence and Bio-computing, Dalian Minzu University, Dalian, China
- Key Lab of Advanced Design and Intelligent Computing (Ministry of Education), Dalian University, Dalian, China
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8
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Zivkovic M, Tair M, K V, Bacanin N, Hubálovský Š, Trojovský P. Novel hybrid firefly algorithm: an application to enhance XGBoost tuning for intrusion detection classification. PeerJ Comput Sci 2022; 8:e956. [PMID: 35634110 PMCID: PMC9137854 DOI: 10.7717/peerj-cs.956] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2022] [Accepted: 04/01/2022] [Indexed: 06/15/2023]
Abstract
The research proposed in this article presents a novel improved version of the widely adopted firefly algorithm and its application for tuning and optimising XGBoost classifier hyper-parameters for network intrusion detection. One of the greatest issues in the domain of network intrusion detection systems are relatively high false positives and false negatives rates. In the proposed study, by using XGBoost classifier optimised with improved firefly algorithm, this challenge is addressed. Based on the established practice from the modern literature, the proposed improved firefly algorithm was first validated on 28 well-known CEC2013 benchmark instances a comparative analysis with the original firefly algorithm and other state-of-the-art metaheuristics was conducted. Afterwards, the devised method was adopted and tested for XGBoost hyper-parameters optimisation and the tuned classifier was tested on the widely used benchmarking NSL-KDD dataset and more recent USNW-NB15 dataset for network intrusion detection. Obtained experimental results prove that the proposed metaheuristics has significant potential in tackling machine learning hyper-parameters optimisation challenge and that it can be used for improving classification accuracy and average precision of network intrusion detection systems.
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Affiliation(s)
| | | | - Venkatachalam K
- Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, Hradec Kralove, Hradec Kralove, Czech Republic
| | | | - Štěpán Hubálovský
- Department of Applied Cybernetics, Faculty of Science, University of Hradec Králové, Hradec Kralove, Hradec Kralove, Czech Republic
| | - Pavel Trojovský
- Department of Mathematics, Faculty of Science, University of Hradec Králové, Hradec Kralove, Hradec Kralove, Czech Republic
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9
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Laxmisagar HS, Hanumantharaju MC. Detection of Breast Cancer with Lightweight Deep Neural Networks for Histology Image Classification. Crit Rev Biomed Eng 2022; 50:1-19. [PMID: 36374820 DOI: 10.1615/critrevbiomedeng.2022043417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Many researchers have developed computer-assisted diagnostic (CAD) methods to diagnose breast cancer using histopathology microscopic images. These techniques help to improve the accuracy of biopsy diagnosis with hematoxylin and eosin-stained images. On the other hand, most CAD systems usually rely on inefficient and time-consuming manual feature extraction methods. Using a deep learning (DL) model with convolutional layers, we present a method to extract the most useful pictorial information for breast cancer classification. Breast biopsy images stained with hematoxylin and eosin can be categorized into four groups namely benign lesions, normal tissue, carcinoma in situ, and invasive carcinoma. To correctly classify different types of breast cancer, it is important to classify histopathological images accurately. The MobileNet architecture model is used to obtain high accuracy with less resource utilization. The proposed model is fast, inexpensive, and safe due to which it is suitable for the detection of breast cancer at an early stage. This lightweight deep neural network can be accelerated using field-programmable gate arrays for the detection of breast cancer. DL has been implemented to successfully classify breast cancer. The model uses categorical cross-entropy to learn to give the correct class a high probability and other classes a low probability. It is used in the classification stage of the convolutional neural network (CNN) after the clustering stage, thereby improving the performance of the proposed system. To measure training and validation accuracy, the model was trained on Google Colab for 280 epochs with a powerful GPU with 2496 CUDA cores, 12 GB GDDR5 VRAM, and 12.6 GB RAM. Our results demonstrate that deep CNN with a chi-square test has improved the accuracy of histopathological image classification of breast cancer by greater than 11% compared with other state-of-the-art methods.
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Affiliation(s)
- H S Laxmisagar
- Department of Electronics and Communication Engineering, BMS Institute of Technology Management, Bengaluru 560064, India
| | - M C Hanumantharaju
- Department of Electronics and Communication Engineering, BMS Institute of Technology Management, Bengaluru 560064, India
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10
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Babu T, Singh T, Gupta D, Hameed S. Colon cancer prediction on histological images using deep learning features and Bayesian optimized SVM. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2021. [DOI: 10.3233/jifs-189850] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Abstract
Colon cancer is one of the highest cancer diagnosis mortality rates worldwide. However, relying on the expertise of pathologists is a demanding and time-consuming process for histopathological analysis. The automated diagnosis of colon cancer from biopsy examination played an important role for patients and prognosis. As conventional handcrafted feature extraction requires specialized experience to select realistic features, deep learning processes have been chosen as abstract high-level features may be extracted automatically. This paper presents the colon cancer detection system using transfer learning architectures to automatically extract high-level features from colon biopsy images for automated diagnosis of patients and prognosis. In this study, the image features are extracted from a pre-trained convolutional neural network (CNN) and used to train the Bayesian optimized Support Vector Machine classifier. Moreover, Alexnet, VGG-16, and Inception-V3 pre-trained neural networks were used to analyze the best network for colon cancer detection. Furthermore, the proposed framework is evaluated using four datasets: two are collected from Indian hospitals (with different magnifications 4X, 10X, 20X, and 40X) and the other two are public colon image datasets. Compared with the existing classifiers and methods using public datasets, the test results evaluated the Inception-V3 network with the accuracy range from 96.5% - 99% as best suited for the proposed framework.
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Affiliation(s)
- Tina Babu
- Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India
| | - Tripty Singh
- Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India
| | - Deepa Gupta
- Department of Computer Science and Engineering, Amrita School of Engineering, Bengaluru, Amrita Vishwa Vidyapeetham, India
| | - Shahin Hameed
- Department of Pathology, MVR Cancer Center and Research Institute, Poolacode, Kerala, India
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11
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Jose L, Liu S, Russo C, Nadort A, Di Ieva A. Generative Adversarial Networks in Digital Pathology and Histopathological Image Processing: A Review. J Pathol Inform 2021; 12:43. [PMID: 34881098 PMCID: PMC8609288 DOI: 10.4103/jpi.jpi_103_20] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 03/03/2021] [Accepted: 04/23/2021] [Indexed: 12/13/2022] Open
Abstract
Digital pathology is gaining prominence among the researchers with developments in advanced imaging modalities and new technologies. Generative adversarial networks (GANs) are a recent development in the field of artificial intelligence and since their inception, have boosted considerable interest in digital pathology. GANs and their extensions have opened several ways to tackle many challenging histopathological image processing problems such as color normalization, virtual staining, ink removal, image enhancement, automatic feature extraction, segmentation of nuclei, domain adaptation and data augmentation. This paper reviews recent advances in histopathological image processing using GANs with special emphasis on the future perspectives related to the use of such a technique. The papers included in this review were retrieved by conducting a keyword search on Google Scholar and manually selecting the papers on the subject of H&E stained digital pathology images for histopathological image processing. In the first part, we describe recent literature that use GANs in various image preprocessing tasks such as stain normalization, virtual staining, image enhancement, ink removal, and data augmentation. In the second part, we describe literature that use GANs for image analysis, such as nuclei detection, segmentation, and feature extraction. This review illustrates the role of GANs in digital pathology with the objective to trigger new research on the application of generative models in future research in digital pathology informatics.
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Affiliation(s)
- Laya Jose
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical
School, Faculty of Medicine, Health and Human Sciences, Macquarie University,
Sydney, Australia
- ARC Centre of Excellence for Nanoscale Biophotonics,
Macquarie University, Sydney, Australia
| | - Sidong Liu
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical
School, Faculty of Medicine, Health and Human Sciences, Macquarie University,
Sydney, Australia
- Australian Institute of Health Innovation, Centre for
Health Informatics, Macquarie University, Sydney, Australia
| | - Carlo Russo
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical
School, Faculty of Medicine, Health and Human Sciences, Macquarie University,
Sydney, Australia
| | - Annemarie Nadort
- ARC Centre of Excellence for Nanoscale Biophotonics,
Macquarie University, Sydney, Australia
- Department of Physics and Astronomy, Faculty of Science
and Engineering, Macquarie University, Sydney, Australia
| | - Antonio Di Ieva
- Computational NeuroSurgery (CNS) Lab, Macquarie Medical
School, Faculty of Medicine, Health and Human Sciences, Macquarie University,
Sydney, Australia
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12
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Hybrid Fruit-Fly Optimization Algorithm with K-Means for Text Document Clustering. MATHEMATICS 2021. [DOI: 10.3390/math9161929] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
The fast-growing Internet results in massive amounts of text data. Due to the large volume of the unstructured format of text data, extracting relevant information and its analysis becomes very challenging. Text document clustering is a text-mining process that partitions the set of text-based documents into mutually exclusive clusters in such a way that documents within the same group are similar to each other, while documents from different clusters differ based on the content. One of the biggest challenges in text clustering is partitioning the collection of text data by measuring the relevance of the content in the documents. Addressing this issue, in this work a hybrid swarm intelligence algorithm with a K-means algorithm is proposed for text clustering. First, the hybrid fruit-fly optimization algorithm is tested on ten unconstrained CEC2019 benchmark functions. Next, the proposed method is evaluated on six standard benchmark text datasets. The experimental evaluation on the unconstrained functions, as well as on text-based documents, indicated that the proposed approach is robust and superior to other state-of-the-art methods.
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13
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Kuntz S, Krieghoff-Henning E, Kather JN, Jutzi T, Höhn J, Kiehl L, Hekler A, Alwers E, von Kalle C, Fröhling S, Utikal JS, Brenner H, Hoffmeister M, Brinker TJ. Gastrointestinal cancer classification and prognostication from histology using deep learning: Systematic review. Eur J Cancer 2021; 155:200-215. [PMID: 34391053 DOI: 10.1016/j.ejca.2021.07.012] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Accepted: 07/06/2021] [Indexed: 02/07/2023]
Abstract
BACKGROUND Gastrointestinal cancers account for approximately 20% of all cancer diagnoses and are responsible for 22.5% of cancer deaths worldwide. Artificial intelligence-based diagnostic support systems, in particular convolutional neural network (CNN)-based image analysis tools, have shown great potential in medical computer vision. In this systematic review, we summarise recent studies reporting CNN-based approaches for digital biomarkers for characterization and prognostication of gastrointestinal cancer pathology. METHODS Pubmed and Medline were screened for peer-reviewed papers dealing with CNN-based gastrointestinal cancer analyses from histological slides, published between 2015 and 2020.Seven hundred and ninety titles and abstracts were screened, and 58 full-text articles were assessed for eligibility. RESULTS Sixteen publications fulfilled our inclusion criteria dealing with tumor or precursor lesion characterization or prognostic and predictive biomarkers: 14 studies on colorectal or rectal cancer, three studies on gastric cancer and none on esophageal cancer. These studies were categorised according to their end-points: polyp characterization, tumor characterization and patient outcome. Regarding the translation into clinical practice, we identified several studies demonstrating generalization of the classifier with external tests and comparisons with pathologists, but none presenting clinical implementation. CONCLUSIONS Results of recent studies on CNN-based image analysis in gastrointestinal cancer pathology are promising, but studies were conducted in observational and retrospective settings. Large-scale trials are needed to assess performance and predict clinical usefulness. Furthermore, large-scale trials are required for approval of CNN-based prediction models as medical devices.
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Affiliation(s)
- Sara Kuntz
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Eva Krieghoff-Henning
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jakob N Kather
- Department of Medicine III, University Hospital RWTH Aachen, Aachen, Germany
| | - Tanja Jutzi
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Julia Höhn
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Lennard Kiehl
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Achim Hekler
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Elizabeth Alwers
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Christof von Kalle
- Department of Clinical-Translational Sciences, Charité University Medicine and Berlin Institute of Health (BIH), Berlin, Germany
| | - Stefan Fröhling
- Department of Translational Medical Oncology, National Center for Tumor Diseases (NCT), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jochen S Utikal
- Department of Dermatology, Heidelberg University, Mannheim, Germany; Skin Cancer Unit, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ), National Center for Tumor Diseases (NCT), Heidelberg, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Titus J Brinker
- Digital Biomarkers for Oncology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany.
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14
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Improved bag-of-features using grey relational analysis for classification of histology images. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-021-00275-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
AbstractAn efficient classification method to categorize histopathological images is a challenging research problem. In this paper, an improved bag-of-features approach is presented as an efficient image classification method. In bag-of-features, a large number of keypoints are extracted from histopathological images that increases the computational cost of the codebook construction step. Therefore, to select the a relevant subset of keypoints, a new keypoints selection method is introduced in the bag-of-features method. To validate the performance of the proposed method, an extensive experimental analysis is conducted on two standard histopathological image datasets, namely ADL and Blue histology datasets. The proposed keypoint selection method reduces the extracted high dimensional features by 95% and 68% from the ADL and Blue histology datasets respectively with less computational time. Moreover, the enhanced bag-of-features method increases classification accuracy by from other considered classification methods.
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Nasir IM, Rashid M, Shah JH, Sharif M, Awan MYH, Alkinani MH. An Optimized Approach for Breast Cancer Classification for Histopathological Images Based on Hybrid Feature Set. Curr Med Imaging 2021; 17:136-147. [PMID: 32324518 DOI: 10.2174/1573405616666200423085826] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2019] [Revised: 03/05/2020] [Accepted: 03/24/2020] [Indexed: 11/22/2022]
Abstract
BACKGROUND Breast cancer is considered as one of the most perilous sickness among females worldwide and the ratio of new cases is increasing yearly. Many researchers have proposed efficient algorithms to diagnose breast cancer at early stages, which have increased the efficiency and performance by utilizing the learned features of gold standard histopathological images. OBJECTIVE Most of these systems have either used traditional handcrafted or deep features, which had a lot of noise and redundancy, and ultimately decrease the performance of the system. METHODS A hybrid approach is proposed by fusing and optimizing the properties of handcrafted and deep features to classify the breast cancer images. HOG and LBP features are serially fused with pre-trained models VGG19 and InceptionV3. PCR and ICR are used to evaluate the classification performance of the proposed method. RESULTS The method concentrates on histopathological images to classify the breast cancer. The performance is compared with the state-of-the-art techniques, where an overall patient-level accuracy of 97.2% and image-level accuracy of 96.7% is recorded. CONCLUSION The proposed hybrid method achieves the best performance as compared to previous methods and it can be used for the intelligent healthcare systems and early breast cancer detection.
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Affiliation(s)
| | - Muhammad Rashid
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan
| | - Jamal Hussain Shah
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan
| | | | - Monagi H Alkinani
- College of Computer Science and Engineering, Department of Computer Science and Artificial Intelligence, University of Jeddah, Saudi Arabia
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Stoean R, Stoean C, Becerra-García R, García-Bermúdez R, Atencia M, García-Lagos F, Velázquez-Pérez L, Joya G. A hybrid unsupervised-Deep learning tandem for electrooculography time series analysis. PLoS One 2020; 15:e0236401. [PMID: 32692779 PMCID: PMC7373280 DOI: 10.1371/journal.pone.0236401] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 07/06/2020] [Indexed: 11/18/2022] Open
Abstract
Medical data are often tricky to get mined for patterns even by the generally demonstrated successful modern methodologies of deep learning. This paper puts forward such a medical classification task, where patient registers of two of the categories are sometimes hard to be distinguished because of samples showing characteristics of both labels in turn in several repetitions of the screening procedure. To this end, the current research appoints a pre-processing clustering step (through self-organizing maps) to group the data based on shape similarity and relabel it accordingly. Subsequently, a deep learning approach (a tandem of convolutional and long short-term memory networks) performs the training classification phase on the ‘cleaned’ samples. The dual methodology was applied for the computational diagnosis of electrooculography tests within spino-cerebral ataxia of type 2. The accuracy obtained for the discrimination into three classes was of 78.24%. The improvement that this duo brings over the deep learner alone does not stem from significantly higher accuracy results when the performance is considered for all classes. The major finding of this combination is that half of the presymptomatic cases were correctly found, in opposition to the single deep model, where this category was sacrificed by the learner in favor of a good accuracy overall. A high accuracy in general is desirable for any medical task, however the correct identification of cases before the symptoms become evident is more important.
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Affiliation(s)
| | | | | | | | | | | | - Luis Velázquez-Pérez
- Cuban Academy of Sciences, La Habana, Cuba
- Center for Research and Rehabilitation of Hereditary Ataxias, Holguín, Cuba
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Stoean C, Paja W, Stoean R, Sandita A. Deep architectures for long-term stock price prediction with a heuristic-based strategy for trading simulations. PLoS One 2019; 14:e0223593. [PMID: 31600306 PMCID: PMC6786832 DOI: 10.1371/journal.pone.0223593] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 09/24/2019] [Indexed: 11/19/2022] Open
Abstract
Stock price prediction is a popular yet challenging task and deep learning provides the means to conduct the mining for the different patterns that trigger its dynamic movement. In this paper, the task is to predict the close price for 25 companies enlisted at the Bucharest Stock Exchange, from a novel data set introduced herein. Towards this scope, two traditional deep learning architectures are designed in comparison: a long short-memory network and a temporal convolutional neural model. Based on their predictions, a trading strategy, whose decision to buy or sell depends on two different thresholds, is proposed. A hill climbing approach selects the optimal values for these parameters. The prediction of the two deep learning representatives used in the subsequent trading strategy leads to distinct facets of gain.
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Affiliation(s)
- Catalin Stoean
- Romanian Institute of Science and Technology, Cluj-Napoca, Romania
- Grupo Ingeniería de Sistemas Integrados (TIC-125), E.T.S.I. Telecomunicación, Universidad de Malaga, Malaga, Spain
- * E-mail:
| | - Wiesław Paja
- Faculty of Mathematics and Natural Sciences, University of Rzeszów, Rzeszów, Poland
| | - Ruxandra Stoean
- Romanian Institute of Science and Technology, Cluj-Napoca, Romania
- Grupo Ingeniería de Sistemas Integrados (TIC-125), E.T.S.I. Telecomunicación, Universidad de Malaga, Malaga, Spain
| | - Adrian Sandita
- Faculty of Sciences, University of Craiova, Craiova, Romania
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