Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
Unprecedented breakthroughs in the development of graphical processing systems have led to great potential for deep learning (DL) algorithms in analyzing visual anatomy from high-resolution medical images. Recently, in digital pathology, the use of DL technologies has drawn a substantial amount of a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/22/10982 |
| _version_ | 1797511267976478720 |
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| author | Lakpa Dorje Tamang Byung Wook Kim |
| author_facet | Lakpa Dorje Tamang Byung Wook Kim |
| author_sort | Lakpa Dorje Tamang |
| collection | DOAJ |
| description | Unprecedented breakthroughs in the development of graphical processing systems have led to great potential for deep learning (DL) algorithms in analyzing visual anatomy from high-resolution medical images. Recently, in digital pathology, the use of DL technologies has drawn a substantial amount of attention for use in the effective diagnosis of various cancer types, especially colorectal cancer (CRC), which is regarded as one of the dominant causes of cancer-related deaths worldwide. This review provides an in-depth perspective on recently published research articles on DL-based CRC diagnosis and prognosis. Overall, we provide a retrospective synopsis of simple image-processing-based and machine learning (ML)-based computer-aided diagnosis (CAD) systems, followed by a comprehensive appraisal of use cases with different types of state-of-the-art DL algorithms for detecting malignancies. We first list multiple standardized and publicly available CRC datasets from two imaging types: colonoscopy and histopathology. Secondly, we categorize the studies based on the different types of CRC detected (tumor tissue, microsatellite instability, and polyps), and we assess the data preprocessing steps and the adopted DL architectures before presenting the optimum diagnostic results. CRC diagnosis with DL algorithms is still in the preclinical phase, and therefore, we point out some open issues and provide some insights into the practicability and development of robust diagnostic systems in future health care and oncology. |
| first_indexed | 2024-03-10T05:42:57Z |
| format | Article |
| id | doaj.art-1d721a870bd540c0af185f4d4b606f2f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T05:42:57Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1d721a870bd540c0af185f4d4b606f2f2023-11-22T22:22:00ZengMDPI AGApplied Sciences2076-34172021-11-0111221098210.3390/app112210982Deep Learning Approaches to Colorectal Cancer Diagnosis: A ReviewLakpa Dorje Tamang0Byung Wook Kim1Department of Information and Communication Engineering, Changwon National University, Changwon 51140, KoreaDepartment of Information and Communication Engineering, Changwon National University, Changwon 51140, KoreaUnprecedented breakthroughs in the development of graphical processing systems have led to great potential for deep learning (DL) algorithms in analyzing visual anatomy from high-resolution medical images. Recently, in digital pathology, the use of DL technologies has drawn a substantial amount of attention for use in the effective diagnosis of various cancer types, especially colorectal cancer (CRC), which is regarded as one of the dominant causes of cancer-related deaths worldwide. This review provides an in-depth perspective on recently published research articles on DL-based CRC diagnosis and prognosis. Overall, we provide a retrospective synopsis of simple image-processing-based and machine learning (ML)-based computer-aided diagnosis (CAD) systems, followed by a comprehensive appraisal of use cases with different types of state-of-the-art DL algorithms for detecting malignancies. We first list multiple standardized and publicly available CRC datasets from two imaging types: colonoscopy and histopathology. Secondly, we categorize the studies based on the different types of CRC detected (tumor tissue, microsatellite instability, and polyps), and we assess the data preprocessing steps and the adopted DL architectures before presenting the optimum diagnostic results. CRC diagnosis with DL algorithms is still in the preclinical phase, and therefore, we point out some open issues and provide some insights into the practicability and development of robust diagnostic systems in future health care and oncology.https://www.mdpi.com/2076-3417/11/22/10982colorectal cancerdigital pathologycomputer-aided diagnosisdeep learning |
| spellingShingle | Lakpa Dorje Tamang Byung Wook Kim Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review Applied Sciences colorectal cancer digital pathology computer-aided diagnosis deep learning |
| title | Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review |
| title_full | Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review |
| title_fullStr | Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review |
| title_full_unstemmed | Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review |
| title_short | Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review |
| title_sort | deep learning approaches to colorectal cancer diagnosis a review |
| topic | colorectal cancer digital pathology computer-aided diagnosis deep learning |
| url | https://www.mdpi.com/2076-3417/11/22/10982 |
| work_keys_str_mv | AT lakpadorjetamang deeplearningapproachestocolorectalcancerdiagnosisareview AT byungwookkim deeplearningapproachestocolorectalcancerdiagnosisareview |