Current - Issue
Original Article
The Role of Machine Learning and Neural Network in Predicting Colorectal Cancer
Mohan M S1
Priyadarshini P N2
Poornachandra K V3
Dr Smitha A S4
1 2 3 Department of Electronics & Communication Engineering, BGS Institute of Technology, Adichunchanagiri University, Mandya, Karnataka, India. 4 Associate Professor, Department of Electronics & Communication Engineering, BGS Institute of Technology, Adichunchanagiri University, Mandya, Karnataka, India
Published Online: May-June 2026
Pages: 254-260
Cite this article
↗ https://www.doi.org/10.59256/ijire.20260703025
References
1. M. Alotaibi, A. Algamdi, H. Alharthi, and M. Jaber, “Advances in colorectal cancer diagnosis using optimal deep feature fusion approach on biomedical images,” Scientific Reports, 2025.
2. Y. Gao, J. Yang, J. Li et al., “Integration of multiomics features for blood-based early detection of colorectal cancer,” Molecular Cancer, 2024.
3. A. Bhuiyan, R. Hassan, and S. Kundu, “Polyp segmentation in colonoscopy images using deep learning models,” Computers in Biology and Medicine, 2025.
4. M. A. Amin and B. K. Paul, “Colon polyps detection from colonoscopy images using deep learning,” arXiv preprint, 2025.
5. M. Spadaccini, A. Hassan, C. Antonelli, and M. Repici, “Artificial intelligence-assisted colonoscopy and colorectal cancer: Current status and future perspectives,” Digestive and Liver Disease, 2024.
2. Y. Gao, J. Yang, J. Li et al., “Integration of multiomics features for blood-based early detection of colorectal cancer,” Molecular Cancer, 2024.
3. A. Bhuiyan, R. Hassan, and S. Kundu, “Polyp segmentation in colonoscopy images using deep learning models,” Computers in Biology and Medicine, 2025.
4. M. A. Amin and B. K. Paul, “Colon polyps detection from colonoscopy images using deep learning,” arXiv preprint, 2025.
5. M. Spadaccini, A. Hassan, C. Antonelli, and M. Repici, “Artificial intelligence-assisted colonoscopy and colorectal cancer: Current status and future perspectives,” Digestive and Liver Disease, 2024.
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