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Rajasekar D, Theja G, Prusty MR, Chinara S. Efficient colorectal polyp segmentation using wavelet transformation and AdaptUNet: A hybrid U-Net. Heliyon 2024; 10:e33655. [PMID: 39040380 PMCID: PMC11261057 DOI: 10.1016/j.heliyon.2024.e33655] [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: 02/22/2024] [Revised: 03/06/2024] [Accepted: 06/25/2024] [Indexed: 07/24/2024] Open
Abstract
The prevalence of colorectal cancer, primarily emerging from polyps, underscores the importance of their early detection in colonoscopy images. Due to the inherent complexity and variability of polyp appearances, the task stands difficult despite recent advances in medical technology. To tackle these challenges, a deep learning model featuring a customized U-Net architecture, AdaptUNet is proposed. Attention mechanisms and skip connections facilitate the effective combination of low-level details and high-level contextual information for accurate polyp segmentation. Further, wavelet transformations are used to extract useful features overlooked in conventional image processing. The model achieves benchmark results with a Dice coefficient of 0.9104, an Intersection over Union (IoU) coefficient of 0.8368, and a Balanced Accuracy of 0.9880 on the CVC-300 dataset. Additionally, it shows exceptional performance on other datasets, including Kvasir-SEG and Etis-LaribDB. Training was performed using the Hyper Kvasir segmented images dataset, further evidencing the model's ability to handle diverse data inputs. The proposed method offers a comprehensive and efficient implementation for polyp detection without compromising performance, thus promising an improved precision and reduction in manual labour for colorectal polyp detection.
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Affiliation(s)
- Devika Rajasekar
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
| | - Girish Theja
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India
| | - Manas Ranjan Prusty
- Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, India
| | - Suchismita Chinara
- Department of Computer Science and Engineering, National Institute of Technology, Rourkela, India
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2
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Hajim WI, Zainudin S, Mohd Daud K, Alheeti K. Optimized models and deep learning methods for drug response prediction in cancer treatments: a review. PeerJ Comput Sci 2024; 10:e1903. [PMID: 38660174 PMCID: PMC11042005 DOI: 10.7717/peerj-cs.1903] [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: 09/05/2023] [Accepted: 01/31/2024] [Indexed: 04/26/2024]
Abstract
Recent advancements in deep learning (DL) have played a crucial role in aiding experts to develop personalized healthcare services, particularly in drug response prediction (DRP) for cancer patients. The DL's techniques contribution to this field is significant, and they have proven indispensable in the medical field. This review aims to analyze the diverse effectiveness of various DL models in making these predictions, drawing on research published from 2017 to 2023. We utilized the VOS-Viewer 1.6.18 software to create a word cloud from the titles and abstracts of the selected studies. This study offers insights into the focus areas within DL models used for drug response. The word cloud revealed a strong link between certain keywords and grouped themes, highlighting terms such as deep learning, machine learning, precision medicine, precision oncology, drug response prediction, and personalized medicine. In order to achieve an advance in DRP using DL, the researchers need to work on enhancing the models' generalizability and interoperability. It is also crucial to develop models that not only accurately represent various architectures but also simplify these architectures, balancing the complexity with the predictive capabilities. In the future, researchers should try to combine methods that make DL models easier to understand; this will make DRP reviews more open and help doctors trust the decisions made by DL models in cancer DRP.
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Affiliation(s)
- Wesam Ibrahim Hajim
- Department of Applied Geology, College of Sciences, Tirkit University, Tikrit, Salah ad Din, Iraq
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia
| | - Suhaila Zainudin
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia
| | - Kauthar Mohd Daud
- Center for Artificial Intelligence Technology, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Selangor, Malaysia
| | - Khattab Alheeti
- Department of Computer Networking Systems, College of Computer Sciences and Information Technology, University of Anbar, Al Anbar, Ramadi, Iraq
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3
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Kim HM, Ko T, Kang H, Choi S, Park JH, Chung MK, Kim M, Kim NY, Lee HJ. Improved prediction of clinical pregnancy using artificial intelligence with enhanced inner cell mass and trophectoderm images. Sci Rep 2024; 14:3240. [PMID: 38331914 PMCID: PMC10853203 DOI: 10.1038/s41598-024-52241-x] [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: 07/26/2023] [Accepted: 01/16/2024] [Indexed: 02/10/2024] Open
Abstract
This study aimed to assess the performance of an artificial intelligence (AI) model for predicting clinical pregnancy using enhanced inner cell mass (ICM) and trophectoderm (TE) images. In this retrospective study, we included static images of 2555 day-5-blastocysts from seven in vitro fertilization centers in South Korea. The main outcome of the study was the predictive capability of the model to detect clinical pregnancies (gestational sac). Compared with the original embryo images, the use of enhanced ICM and TE images improved the average area under the receiver operating characteristic curve for the AI model from 0.716 to 0.741. Additionally, a gradient-weighted class activation mapping analysis demonstrated that the enhanced image-trained AI model was able to extract features from crucial areas of the embryo in 99% (506/512) of the cases. Particularly, it could extract the ICM and TE. In contrast, the AI model trained on the original images focused on the main areas in only 86% (438/512) of the cases. Our results highlight the potential efficacy of using ICM- and TE-enhanced embryo images when training AI models to predict clinical pregnancy.
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Affiliation(s)
| | - Taehoon Ko
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, South Korea
- Department of Biomedicine & Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, South Korea
- CMC Institute for Basic Medical Science, The Catholic Medical Center of The Catholic University of Korea, Seoul, South Korea
| | | | | | | | - Mi Kyung Chung
- Seoul Rachel Fertility Center, IVF Clinic, Seoul, South Korea
| | - Miran Kim
- Department of Obstetrics & Gynecology, Ajou University School of Medicine, Suwon, South Korea
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Badawy M, Balaha HM, Maklad AS, Almars AM, Elhosseini MA. Revolutionizing Oral Cancer Detection: An Approach Using Aquila and Gorilla Algorithms Optimized Transfer Learning-Based CNNs. Biomimetics (Basel) 2023; 8:499. [PMID: 37887629 PMCID: PMC10604828 DOI: 10.3390/biomimetics8060499] [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: 08/18/2023] [Revised: 10/11/2023] [Accepted: 10/17/2023] [Indexed: 10/28/2023] Open
Abstract
The early detection of oral cancer is pivotal for improving patient survival rates. However, the high cost of manual initial screenings poses a challenge, especially in resource-limited settings. Deep learning offers an enticing solution by enabling automated and cost-effective screening. This study introduces a groundbreaking empirical framework designed to revolutionize the accurate and automatic classification of oral cancer using microscopic histopathology slide images. This innovative system capitalizes on the power of convolutional neural networks (CNNs), strengthened by the synergy of transfer learning (TL), and further fine-tuned using the novel Aquila Optimizer (AO) and Gorilla Troops Optimizer (GTO), two cutting-edge metaheuristic optimization algorithms. This integration is a novel approach, addressing bias and unpredictability issues commonly encountered in the preprocessing and optimization phases. In the experiments, the capabilities of well-established pre-trained TL models, including VGG19, VGG16, MobileNet, MobileNetV3Small, MobileNetV2, MobileNetV3Large, NASNetMobile, and DenseNet201, all initialized with 'ImageNet' weights, were harnessed. The experimental dataset consisted of the Histopathologic Oral Cancer Detection dataset, which includes a 'normal' class with 2494 images and an 'OSCC' (oral squamous cell carcinoma) class with 2698 images. The results reveal a remarkable performance distinction between the AO and GTO, with the AO consistently outperforming the GTO across all models except for the Xception model. The DenseNet201 model stands out as the most accurate, achieving an astounding average accuracy rate of 99.25% with the AO and 97.27% with the GTO. This innovative framework signifies a significant leap forward in automating oral cancer detection, showcasing the tremendous potential of applying optimized deep learning models in the realm of healthcare diagnostics. The integration of the AO and GTO in our CNN-based system not only pushes the boundaries of classification accuracy but also underscores the transformative impact of metaheuristic optimization techniques in the field of medical image analysis.
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Affiliation(s)
- Mahmoud Badawy
- Department of Computer Science and Informatics, Applied College, Taibah University, Al Madinah Al Munawwarah 41461, Saudi Arabia
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt (M.A.E.)
| | - Hossam Magdy Balaha
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt (M.A.E.)
- Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY 40208, USA
| | - Ahmed S. Maklad
- College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; (A.S.M.); (A.M.A.)
- Information Systems Department, Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suif 62521, Egypt
| | - Abdulqader M. Almars
- College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; (A.S.M.); (A.M.A.)
| | - Mostafa A. Elhosseini
- Department of Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt (M.A.E.)
- College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia; (A.S.M.); (A.M.A.)
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5
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Cooper M, Ji Z, Krishnan RG. Machine learning in computational histopathology: Challenges and opportunities. Genes Chromosomes Cancer 2023; 62:540-556. [PMID: 37314068 DOI: 10.1002/gcc.23177] [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: 01/31/2023] [Revised: 05/18/2023] [Accepted: 05/20/2023] [Indexed: 06/15/2023] Open
Abstract
Digital histopathological images, high-resolution images of stained tissue samples, are a vital tool for clinicians to diagnose and stage cancers. The visual analysis of patient state based on these images are an important part of oncology workflow. Although pathology workflows have historically been conducted in laboratories under a microscope, the increasing digitization of histopathological images has led to their analysis on computers in the clinic. The last decade has seen the emergence of machine learning, and deep learning in particular, a powerful set of tools for the analysis of histopathological images. Machine learning models trained on large datasets of digitized histopathology slides have resulted in automated models for prediction and stratification of patient risk. In this review, we provide context for the rise of such models in computational histopathology, highlight the clinical tasks they have found success in automating, discuss the various machine learning techniques that have been applied to this domain, and underscore open problems and opportunities.
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Affiliation(s)
- Michael Cooper
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- University Health Network, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Zongliang Ji
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
| | - Rahul G Krishnan
- Department of Computer Science, University of Toronto, Toronto, Ontario, Canada
- Vector Institute, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
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6
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AlGhamdi R, Asar TO, Assiri FY, Mansouri RA, Ragab M. Al-Biruni Earth Radius Optimization with Transfer Learning Based Histopathological Image Analysis for Lung and Colon Cancer Detection. Cancers (Basel) 2023; 15:3300. [PMID: 37444410 DOI: 10.3390/cancers15133300] [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: 06/01/2023] [Revised: 06/13/2023] [Accepted: 06/14/2023] [Indexed: 07/15/2023] Open
Abstract
An early diagnosis of lung and colon cancer (LCC) is critical for improved patient outcomes and effective treatment. Histopathological image (HSI) analysis has emerged as a robust tool for cancer diagnosis. HSI analysis for a LCC diagnosis includes the analysis and examination of tissue samples attained from the LCC to recognize lesions or cancerous cells. It has a significant role in the staging and diagnosis of this tumor, which aids in the prognosis and treatment planning, but a manual analysis of the image is subject to human error and is also time-consuming. Therefore, a computer-aided approach is needed for the detection of LCC using HSI. Transfer learning (TL) leverages pretrained deep learning (DL) algorithms that have been trained on a larger dataset for extracting related features from the HIS, which are then used for training a classifier for a tumor diagnosis. This manuscript offers the design of the Al-Biruni Earth Radius Optimization with Transfer Learning-based Histopathological Image Analysis for Lung and Colon Cancer Detection (BERTL-HIALCCD) technique. The purpose of the study is to detect LCC effectually in histopathological images. To execute this, the BERTL-HIALCCD method follows the concepts of computer vision (CV) and transfer learning for accurate LCC detection. When using the BERTL-HIALCCD technique, an improved ShuffleNet model is applied for the feature extraction process, and its hyperparameters are chosen by the BER system. For the effectual recognition of LCC, a deep convolutional recurrent neural network (DCRNN) model is applied. Finally, the coati optimization algorithm (COA) is exploited for the parameter choice of the DCRNN approach. For examining the efficacy of the BERTL-HIALCCD technique, a comprehensive group of experiments was conducted on a large dataset of histopathological images. The experimental outcomes demonstrate that the combination of AER and COA algorithms attain an improved performance in cancer detection over the compared models.
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Affiliation(s)
- Rayed AlGhamdi
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Turky Omar Asar
- Department of Biology, College of Science and Arts at Alkamil, University of Jeddah, Jeddah, Saudi Arabia
| | - Fatmah Y Assiri
- Department of Software Engineering, College of Computer Science and Engineering, University of Jeddah, Jeddah, Saudi Arabia
| | - Rasha A Mansouri
- Prince Sattam Bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Mahmoud Ragab
- Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
- Mathematics Department, Faculty of Science, Al-Azhar University, Naser City 11884, Cairo, Egypt
- King Abdulaziz University-University of Oxford Centre for Artificial Intelligence in Precision Medicines, King Abdulaziz University, Jeddah 21589, Saudi Arabia
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7
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Ehsaeyan E. An efficient image segmentation method based on expectation maximization and Salp swarm algorithm. MULTIMEDIA TOOLS AND APPLICATIONS 2023:1-31. [PMID: 37362643 PMCID: PMC10061417 DOI: 10.1007/s11042-023-15149-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 11/14/2022] [Accepted: 03/13/2023] [Indexed: 06/28/2023]
Abstract
Multilevel image thresholding using Expectation Maximization (EM) is an efficient method for image segmentation. However, it has two weaknesses: 1) EM is a greedy algorithm and cannot jump out of local optima. 2) it cannot guarantee the number of required classes while estimating the histogram by Gaussian Mixture Models (GMM). in this paper, to overcome these shortages, a novel thresholding approach by combining EM and Salp Swarm Algorithm (SSA) is developed. SSA suggests potential points to the EM algorithm to fly to a better position. Moreover, a new mechanism is considered to maintain the number of desired clusters. Twenty-four medical test images are selected and examined by standard metrics such as PSNR and FSIM. The proposed method is compared with the traditional EM algorithm, and an average improvement of 5.27% in PSNR values and 2.01% in FSIM values were recorded. Also, the proposed approach is compared with four existing segmentation techniques by using CT scan images that Qatar University has collected. Experimental results depict that the proposed method obtains the first rank in terms of PSNR and the second rank in terms of FSIM. It has been observed that the proposed technique performs better performance in the segmentation result compared to other considered state-of-the-art methods.
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Affiliation(s)
- Ehsan Ehsaeyan
- Electrical Engineering Department, Sirjan University of Technology, Sirjan, Iran
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8
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Zhang Q, Liang Y, Zhang Y, Tao Z, Li R, Bi H. A comparative study of attention mechanism based deep learning methods for bladder tumor segmentation. Int J Med Inform 2023; 171:104984. [PMID: 36634475 DOI: 10.1016/j.ijmedinf.2023.104984] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Revised: 12/17/2022] [Accepted: 01/01/2023] [Indexed: 01/07/2023]
Abstract
BACKGROUND Artificial intelligence aided tumor segmentation has been applied in various medical scenarios and showed effectiveness in helping physicians observe the potential malignant tissues. However, little research has been conducted for the cystoscopic image segmentation problem. METHODS This paper provided a comprehensive comparison of various attention modules for improving the bladder tumor segmentation performance by utilizing the cystoscopic images from Peking University Third Hospital within 2017-2022. Furthermore, this paper presented an attention mechanism based cystoscopic images segmentation (ACS) model, which was featured by the following points: (1) A mixed attention module including both the channel and spatial attention modules was integrated in the encoder-decoder path, which helped to exploit the global information of the tumor area more effectively. (2) A guidance and fusion attention module was introduced in the skip connection part, facilitating the integration of the high-level semantic features with low-level fine-grained features and the discarding of irrelevant features. (3) An inception attention module was added to enhance the feature expression in the scale of pixel level, so as to better discriminate multi-scale targets. RESULTS The proposed ACS model showed obviously better tumor segmentation performance than the compared models, with Dice of 82.7% and MIoU of 69% achieved. CONCLUSIONS The proposed ACS model achieved significantly better diagnostic performance than the previous bladder tumor segmentation method based on U-Net. Our ACS model is expected to be a useful support tool to assist the tumor segmentation under cystoscopy.
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Affiliation(s)
- Qi Zhang
- School of Information Technology & Management, University of International Business & Economics, Beijing 100029, China
| | - Yinglu Liang
- School of Information Technology & Management, University of International Business & Economics, Beijing 100029, China
| | - Yi Zhang
- School of Information Technology & Management, University of International Business & Economics, Beijing 100029, China
| | - Zihao Tao
- Department of Urology, Peking University Third Hospital, Beijing 100191, China
| | - Rui Li
- School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China.
| | - Hai Bi
- Department of Urology, Peking University Third Hospital, Beijing 100191, China.
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Haider A, Arsalan M, Hyun Nam S, Sultan H, Ryoung Park K. Computer-aided Fish Assessment in an Underwater Marine Environment Using Parallel and Progressive Spatial Information Fusion. JOURNAL OF KING SAUD UNIVERSITY - COMPUTER AND INFORMATION SCIENCES 2023. [DOI: 10.1016/j.jksuci.2023.02.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/24/2023]
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10
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Liu A, Li X, Wu H, Guo B, Jonnagaddala J, Zhang H, Xu S. Prognostic Significance of Tumor-Infiltrating Lymphocytes Determined Using LinkNet on Colorectal Cancer Pathology Images. JCO Precis Oncol 2023; 7:e2200522. [PMID: 36848612 DOI: 10.1200/po.22.00522] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/01/2023] Open
Abstract
PURPOSE Tumor-infiltrating lymphocytes (TILs) have a significant prognostic value in cancers. However, very few automated, deep learning-based TIL scoring algorithms have been developed for colorectal cancer (CRC). MATERIALS AND METHODS We developed an automated, multiscale LinkNet workflow for quantifying TILs at the cellular level in CRC tumors using H&E-stained images from the Lizard data set with annotations of lymphocytes. The predictive performance of the automatic TIL scores (TILsLink) for disease progression and overall survival (OS) was evaluated using two international data sets, including 554 patients with CRC from The Cancer Genome Atlas (TCGA) and 1,130 patients with CRC from Molecular and Cellular Oncology (MCO). RESULTS The LinkNet model provided outstanding precision (0.9508), recall (0.9185), and overall F1 score (0.9347). Clear continuous TIL-hazard relationships were observed between TILsLink and the risk of disease progression or death in both TCGA and MCO cohorts. Both univariate and multivariate Cox regression analyses for the TCGA data demonstrated that patients with high TIL abundance had a significant (approximately 75%) reduction in risk for disease progression. In both the MCO and TCGA cohorts, the TIL-high group was significantly associated with improved OS in univariate analysis (30% and 54% reduction in risk, respectively). The favorable effects of high TIL levels were consistently observed in different subgroups (classified according to known risk factors). CONCLUSION The proposed deep-learning workflow for automatic TIL quantification on the basis of LinkNet can be a useful tool for CRC. TILsLink is likely an independent risk factor for disease progression and carries predictive information of disease progression beyond the current clinical risk factors and biomarkers. The prognostic significance of TILsLink for OS is also evident.
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Affiliation(s)
- Anran Liu
- Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, Anhui, China
| | - Xingyu Li
- Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, Anhui, China
| | - Hongyi Wu
- Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, Anhui, China
| | - Bangwei Guo
- School of Data Science, University of Science and Technology of China, Hefei, Anhui, China
| | | | - Hong Zhang
- Department of Statistics and Finance, School of Management, University of Science and Technology of China, Hefei, Anhui, China
| | - Steven Xu
- Clinical Pharmacology and Quantitative Science, Genmab Inc, Princeton, NJ
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11
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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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12
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Ramzan M, Raza M, Sharif MI, Kadry S. Gastrointestinal Tract Polyp Anomaly Segmentation on Colonoscopy Images Using Graft-U-Net. J Pers Med 2022; 12:jpm12091459. [PMID: 36143244 PMCID: PMC9503374 DOI: 10.3390/jpm12091459] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2022] [Revised: 08/28/2022] [Accepted: 09/01/2022] [Indexed: 11/21/2022] Open
Abstract
Computer-aided polyp segmentation is a crucial task that supports gastroenterologists in examining and resecting anomalous tissue in the gastrointestinal tract. The disease polyps grow mainly in the colorectal area of the gastrointestinal tract and in the mucous membrane, which has protrusions of micro-abnormal tissue that increase the risk of incurable diseases such as cancer. So, the early examination of polyps can decrease the chance of the polyps growing into cancer, such as adenomas, which can change into cancer. Deep learning-based diagnostic systems play a vital role in diagnosing diseases in the early stages. A deep learning method, Graft-U-Net, is proposed to segment polyps using colonoscopy frames. Graft-U-Net is a modified version of UNet, which comprises three stages, including the preprocessing, encoder, and decoder stages. The preprocessing technique is used to improve the contrast of the colonoscopy frames. Graft-U-Net comprises encoder and decoder blocks where the encoder analyzes features, while the decoder performs the features’ synthesizing processes. The Graft-U-Net model offers better segmentation results than existing deep learning models. The experiments were conducted using two open-access datasets, Kvasir-SEG and CVC-ClinicDB. The datasets were prepared from the large bowel of the gastrointestinal tract by performing a colonoscopy procedure. The anticipated model outperforms in terms of its mean Dice of 96.61% and mean Intersection over Union (mIoU) of 82.45% with the Kvasir-SEG dataset. Similarly, with the CVC-ClinicDB dataset, the method achieved a mean Dice of 89.95% and an mIoU of 81.38%.
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Affiliation(s)
- Muhammad Ramzan
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad 47040, Pakistan
| | - Mudassar Raza
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad 47040, Pakistan
- Correspondence:
| | - Muhammad Imran Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Islamabad 47040, Pakistan
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 999095, Lebanon
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13
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Deep Learning on Histopathological Images for Colorectal Cancer Diagnosis: A Systematic Review. Diagnostics (Basel) 2022; 12:diagnostics12040837. [PMID: 35453885 PMCID: PMC9028395 DOI: 10.3390/diagnostics12040837] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Revised: 03/22/2022] [Accepted: 03/25/2022] [Indexed: 02/04/2023] Open
Abstract
Colorectal cancer (CRC) is the second most common cancer in women and the third most common in men, with an increasing incidence. Pathology diagnosis complemented with prognostic and predictive biomarker information is the first step for personalized treatment. The increased diagnostic load in the pathology laboratory, combined with the reported intra- and inter-variability in the assessment of biomarkers, has prompted the quest for reliable machine-based methods to be incorporated into the routine practice. Recently, Artificial Intelligence (AI) has made significant progress in the medical field, showing potential for clinical applications. Herein, we aim to systematically review the current research on AI in CRC image analysis. In histopathology, algorithms based on Deep Learning (DL) have the potential to assist in diagnosis, predict clinically relevant molecular phenotypes and microsatellite instability, identify histological features related to prognosis and correlated to metastasis, and assess the specific components of the tumor microenvironment.
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