Assessment of a Deep-Learning System for Colorectal Cancer Diagnosis Using Histopathology Images

Purna Kar; Sareh Rowlands

doi:doi:10.11648/j.ajcst.20240703.14

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Assessment of a Deep-Learning System for Colorectal Cancer Diagnosis Using Histopathology Images

Purna Kar

, Sareh Rowlands^*

Published in American Journal of Computer Science and Technology (Volume 7, Issue 3)

Received: 6 August 2024 Accepted: 2 September 2024 Published: 20 September 2024

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Abstract

Colorectal Cancer is one of the most common and lethal forms of cancer hence, an early and accurate detection is crucial. Traditional manual diagnosis is a tedious and time-consuming job susceptible to human errors; therefore, it is imperative to use computer-aided detection systems to interpret medical images for a quicker and more accurate diagnosis. In recent years deep-learning approaches have proved to be efficacious in predicting cancer from pathological images. This study assesses several deep-learning techniques for cancer diagnosis on digitized histopathology images, amongst which GoogLeNet and Xception emerged as the most effective, with GoogLeNet exhibiting slightly better precision in identifying cancerous tissues. Building on these findings the study proposes a new model (Xception+) by borrowing the idea from Xception architecture, which outperforms existing architectures with an accuracy of 99.37% for cancer diagnosis and 94.48% for cancer-grade classification. The primary inference of our research is assisting pathologists in detecting colorectal cancer from pathological images faster and more accurately. With notable accuracy and robustness, our proposed model has significant potential to analyze pathological images and detect the patterns associated with other types of cancer. Our study holds promise for driving the advancement of innovative medical diagnostic tools, aiding pathologists and medical practitioners in expediting cancer diagnosis processes.

Published in	American Journal of Computer Science and Technology (Volume 7, Issue 3)
DOI	10.11648/j.ajcst.20240703.14
Page(s)	90-103
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Artificial Intelligence, Cancer Detection, Colorectal Cancer, Convolutional Neural Networks, Deep Neural Networks

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Cite This Article

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APA Style

Kar, P., Rowlands, S. (2024). Assessment of a Deep-Learning System for Colorectal Cancer Diagnosis Using Histopathology Images. American Journal of Computer Science and Technology, 7(3), 90-103. https://doi.org/10.11648/j.ajcst.20240703.14

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ACS Style

Kar, P.; Rowlands, S. Assessment of a Deep-Learning System for Colorectal Cancer Diagnosis Using Histopathology Images. Am. J. Comput. Sci. Technol. 2024, 7(3), 90-103. doi: 10.11648/j.ajcst.20240703.14

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AMA Style

Kar P, Rowlands S. Assessment of a Deep-Learning System for Colorectal Cancer Diagnosis Using Histopathology Images. Am J Comput Sci Technol. 2024;7(3):90-103. doi: 10.11648/j.ajcst.20240703.14

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@article{10.11648/j.ajcst.20240703.14,
  author = {Purna Kar and Sareh Rowlands},
  title = {Assessment of a Deep-Learning System for Colorectal Cancer Diagnosis Using Histopathology Images
},
  journal = {American Journal of Computer Science and Technology},
  volume = {7},
  number = {3},
  pages = {90-103},
  doi = {10.11648/j.ajcst.20240703.14},
  url = {https://doi.org/10.11648/j.ajcst.20240703.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20240703.14},
  abstract = {Colorectal Cancer is one of the most common and lethal forms of cancer hence, an early and accurate detection is crucial. Traditional manual diagnosis is a tedious and time-consuming job susceptible to human errors; therefore, it is imperative to use computer-aided detection systems to interpret medical images for a quicker and more accurate diagnosis. In recent years deep-learning approaches have proved to be efficacious in predicting cancer from pathological images. This study assesses several deep-learning techniques for cancer diagnosis on digitized histopathology images, amongst which GoogLeNet and Xception emerged as the most effective, with GoogLeNet exhibiting slightly better precision in identifying cancerous tissues. Building on these findings the study proposes a new model (Xception+) by borrowing the idea from Xception architecture, which outperforms existing architectures with an accuracy of 99.37% for cancer diagnosis and 94.48% for cancer-grade classification. The primary inference of our research is assisting pathologists in detecting colorectal cancer from pathological images faster and more accurately. With notable accuracy and robustness, our proposed model has significant potential to analyze pathological images and detect the patterns associated with other types of cancer. Our study holds promise for driving the advancement of innovative medical diagnostic tools, aiding pathologists and medical practitioners in expediting cancer diagnosis processes.
},
 year = {2024}
}

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TY  - JOUR
T1  - Assessment of a Deep-Learning System for Colorectal Cancer Diagnosis Using Histopathology Images

AU  - Purna Kar
AU  - Sareh Rowlands
Y1  - 2024/09/20
PY  - 2024
N1  - https://doi.org/10.11648/j.ajcst.20240703.14
DO  - 10.11648/j.ajcst.20240703.14
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 90
EP  - 103
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20240703.14
AB  - Colorectal Cancer is one of the most common and lethal forms of cancer hence, an early and accurate detection is crucial. Traditional manual diagnosis is a tedious and time-consuming job susceptible to human errors; therefore, it is imperative to use computer-aided detection systems to interpret medical images for a quicker and more accurate diagnosis. In recent years deep-learning approaches have proved to be efficacious in predicting cancer from pathological images. This study assesses several deep-learning techniques for cancer diagnosis on digitized histopathology images, amongst which GoogLeNet and Xception emerged as the most effective, with GoogLeNet exhibiting slightly better precision in identifying cancerous tissues. Building on these findings the study proposes a new model (Xception+) by borrowing the idea from Xception architecture, which outperforms existing architectures with an accuracy of 99.37% for cancer diagnosis and 94.48% for cancer-grade classification. The primary inference of our research is assisting pathologists in detecting colorectal cancer from pathological images faster and more accurately. With notable accuracy and robustness, our proposed model has significant potential to analyze pathological images and detect the patterns associated with other types of cancer. Our study holds promise for driving the advancement of innovative medical diagnostic tools, aiding pathologists and medical practitioners in expediting cancer diagnosis processes.

VL  - 7
IS  - 3
ER  -

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Author Information

Purna Kar

School of Architecture Design and the Built Environment, Nottingham Trent University, Nottingham, United Kingdom

Contact Email

http://orcid.org/0009-0004-4230-2047
Sareh Rowlands

College of Engineering, Mathematics, and Physical Sciences, University of Exeter, Exeter, United Kingdom

Contact Email

http://orcid.org/0000-0001-6783-3064

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Plain Text BibTeX RIS

APA Style

Kar, P., Rowlands, S. (2024). Assessment of a Deep-Learning System for Colorectal Cancer Diagnosis Using Histopathology Images. American Journal of Computer Science and Technology, 7(3), 90-103. https://doi.org/10.11648/j.ajcst.20240703.14

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ACS Style

Kar, P.; Rowlands, S. Assessment of a Deep-Learning System for Colorectal Cancer Diagnosis Using Histopathology Images. Am. J. Comput. Sci. Technol. 2024, 7(3), 90-103. doi: 10.11648/j.ajcst.20240703.14

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AMA Style

Kar P, Rowlands S. Assessment of a Deep-Learning System for Colorectal Cancer Diagnosis Using Histopathology Images. Am J Comput Sci Technol. 2024;7(3):90-103. doi: 10.11648/j.ajcst.20240703.14

Copy | Download

@article{10.11648/j.ajcst.20240703.14,
  author = {Purna Kar and Sareh Rowlands},
  title = {Assessment of a Deep-Learning System for Colorectal Cancer Diagnosis Using Histopathology Images
},
  journal = {American Journal of Computer Science and Technology},
  volume = {7},
  number = {3},
  pages = {90-103},
  doi = {10.11648/j.ajcst.20240703.14},
  url = {https://doi.org/10.11648/j.ajcst.20240703.14},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajcst.20240703.14},
  abstract = {Colorectal Cancer is one of the most common and lethal forms of cancer hence, an early and accurate detection is crucial. Traditional manual diagnosis is a tedious and time-consuming job susceptible to human errors; therefore, it is imperative to use computer-aided detection systems to interpret medical images for a quicker and more accurate diagnosis. In recent years deep-learning approaches have proved to be efficacious in predicting cancer from pathological images. This study assesses several deep-learning techniques for cancer diagnosis on digitized histopathology images, amongst which GoogLeNet and Xception emerged as the most effective, with GoogLeNet exhibiting slightly better precision in identifying cancerous tissues. Building on these findings the study proposes a new model (Xception+) by borrowing the idea from Xception architecture, which outperforms existing architectures with an accuracy of 99.37% for cancer diagnosis and 94.48% for cancer-grade classification. The primary inference of our research is assisting pathologists in detecting colorectal cancer from pathological images faster and more accurately. With notable accuracy and robustness, our proposed model has significant potential to analyze pathological images and detect the patterns associated with other types of cancer. Our study holds promise for driving the advancement of innovative medical diagnostic tools, aiding pathologists and medical practitioners in expediting cancer diagnosis processes.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Assessment of a Deep-Learning System for Colorectal Cancer Diagnosis Using Histopathology Images

AU  - Purna Kar
AU  - Sareh Rowlands
Y1  - 2024/09/20
PY  - 2024
N1  - https://doi.org/10.11648/j.ajcst.20240703.14
DO  - 10.11648/j.ajcst.20240703.14
T2  - American Journal of Computer Science and Technology
JF  - American Journal of Computer Science and Technology
JO  - American Journal of Computer Science and Technology
SP  - 90
EP  - 103
PB  - Science Publishing Group
SN  - 2640-012X
UR  - https://doi.org/10.11648/j.ajcst.20240703.14
AB  - Colorectal Cancer is one of the most common and lethal forms of cancer hence, an early and accurate detection is crucial. Traditional manual diagnosis is a tedious and time-consuming job susceptible to human errors; therefore, it is imperative to use computer-aided detection systems to interpret medical images for a quicker and more accurate diagnosis. In recent years deep-learning approaches have proved to be efficacious in predicting cancer from pathological images. This study assesses several deep-learning techniques for cancer diagnosis on digitized histopathology images, amongst which GoogLeNet and Xception emerged as the most effective, with GoogLeNet exhibiting slightly better precision in identifying cancerous tissues. Building on these findings the study proposes a new model (Xception+) by borrowing the idea from Xception architecture, which outperforms existing architectures with an accuracy of 99.37% for cancer diagnosis and 94.48% for cancer-grade classification. The primary inference of our research is assisting pathologists in detecting colorectal cancer from pathological images faster and more accurately. With notable accuracy and robustness, our proposed model has significant potential to analyze pathological images and detect the patterns associated with other types of cancer. Our study holds promise for driving the advancement of innovative medical diagnostic tools, aiding pathologists and medical practitioners in expediting cancer diagnosis processes.

VL  - 7
IS  - 3
ER  -

Copy | Download