RESEARCH ARTICLE


Classification of Colorectal Cancer using ResNet and EfficientNet Models



Abhishek1, Abhishek Ranjan1, Priyanshu Srivastva1, B Prabadevi1, *, Sivakumar Rajagopal2, Rahul Soangra3, Shamala K. Subramaniam4
1 School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India
2 Department of Sensor and Biomedical Technology, Vellore Institute of Technology, Vellore, India
3 Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA 92866, USA
4 Department of Communication Technology and Networks, Universiti Putra Malaysia, Serdang 43400, Malaysia


Article Metrics

CrossRef Citations:
0
Total Statistics:

Full-Text HTML Views: 541
Abstract HTML Views: 294
PDF Downloads: 137
ePub Downloads: 66
Total Views/Downloads: 1038
Unique Statistics:

Full-Text HTML Views: 317
Abstract HTML Views: 179
PDF Downloads: 110
ePub Downloads: 50
Total Views/Downloads: 656



Creative Commons License
© 2024 The Author(s). Published by Bentham Open.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, India; E-mail: prabadevi.b@vit.ac.in


Abstract

Introduction

Cancer is one of the most prevalent diseases from children to elderly adults. This will be deadly if not detected at an earlier stage of the cancerous cell formation, thereby increasing the mortality rate. One such cancer is colorectal cancer, caused due to abnormal growth in the rectum or colon. Early screening of colorectal cancer helps to identify these abnormal growth and can exterminate them before they turn into cancerous cells.

Aim

Therefore, this study aims to develop a robust and efficient classification system for colorectal cancer through Convolutional Neural Networks (CNNs) on histological images.

Methods

Despite challenges in optimizing model architectures, the improved CNN models like ResNet34 and EfficientNet34 could enhance Colorectal Cancer classification accuracy and efficiency, aiding doctors in early detection and diagnosis, ultimately leading to better patient outcomes.

Results

ResNet34 outperforms the EfficientNet34.

Conclusion

The results are compared with other models in the literature, and ResNet34 outperforms all the other models.

Keywords: Deep learning, CNN, ResNet34, EfficientNetB4, Colorectal cancer, Histology, Optimizer, Transforms, Learning rate, Loss function, ROC curve.