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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Autores principales:	Lakpa Dorje Tamang, Byung Wook Kim
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	colorectal cancer digital pathology computer-aided diagnosis deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/1d721a870bd540c0af185f4d4b606f2f
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spelling	oai:doaj.org-article:1d721a870bd540c0af185f4d4b606f2f2021-11-25T16:42:39ZDeep Learning Approaches to Colorectal Cancer Diagnosis: A Review10.3390/app1122109822076-3417https://doaj.org/article/1d721a870bd540c0af185f4d4b606f2f2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/22/10982https://doaj.org/toc/2076-3417Unprecedented 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.Lakpa Dorje TamangByung Wook KimMDPI AGarticlecolorectal cancerdigital pathologycomputer-aided diagnosisdeep learningTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10982, p 10982 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	colorectal cancer digital pathology computer-aided diagnosis deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	colorectal cancer digital pathology computer-aided diagnosis deep learning Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Lakpa Dorje Tamang Byung Wook Kim Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
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.
format	article
author	Lakpa Dorje Tamang Byung Wook Kim
author_facet	Lakpa Dorje Tamang Byung Wook Kim
author_sort	Lakpa Dorje Tamang
title	Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review
title_short	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_sort	deep learning approaches to colorectal cancer diagnosis: a review
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1d721a870bd540c0af185f4d4b606f2f
work_keys_str_mv	AT lakpadorjetamang deeplearningapproachestocolorectalcancerdiagnosisareview AT byungwookkim deeplearningapproachestocolorectalcancerdiagnosisareview
_version_	1718413003903205376

Deep Learning Approaches to Colorectal Cancer Diagnosis: A Review

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