Developing a Machine Learning Model for Colon Cancer Detection from Colonoscopy Data
DOI:
https://doi.org/10.32628/IJSRST251243Abstract
Colon cancer is one of the leading causes of cancer- related deaths globally. Early detection and accurate diagnosis are crucial for improving patient outcomes and survival rates. Colonoscopy remains the gold standard for detecting colon cancer,yet the process is highly dependent on the expertise of the physician and can be time-consuming. In this project, we aim to develop an automated machine learning model for the detection of colon cancer from colonoscopy images and videos. We explore various machine learning techniques, including Con- volutional Neural Networks (CNNs), to analyze colonoscopy data for the identification of polyps, tumors, and other abnormalities indicative of cancerous growths. The dataset used includes a large set of labeled colonoscopy images, and the model is trained to classify the presence of cancerous lesions, distinguishing between benign and malignant cases. Data preprocessing techniques, such as image normalization, augmentation, and segmentation, are employed to improve the accuracy and robustness of the model. The performance of the model is evaluated using standard metrics, including accuracy, precision, recall, and F1 score, with a particular focus on its ability to generalize to unseen data. Preliminary results demonstrate that machine learning models, particularly deep learning approaches, can effectively assist in early colon cancer detection, reducing the burden on healthcare professionals and providing faster, more accurate diagnoses. This research highlights the potential of AI-driven tools in improving colorectal cancer screening processes, ultimately contributing to the reduction of mortality rates and enhancing patient care.
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References
Bulut. B, E. B utun and M. Kaya, ”Polyp Segmentation in Colonoscopy Images using U-Net and Cyclic Learning Rate,” 2022 International Conference on Decision Aid Sciences and Appli- cations (DASA), Chiangrai, Thailand, 2022, pp. 1149-1152, doi: 10.1109/DASA54658.2022.9765101.
Ciobanu. A, M. Luca, R. Vulpoi, O. Barboi and V. Drug, ”Deep Learning in Colonoscopies: Improving Small Polyps Recognition Rate,” 2022 E- Health and Bioengineering Conference (EHB), Iasi, Romania, 2022, pp. 1-4, doi: 10.1109/EHB55594.2022.9991415
De Souza et al., ”Polyp Detection in Colonoscopy Images Using a Vision Transformer Classifier,” 2023 15th IEEE International Conference on Industry Applications (INDUSCON), Sa˜o Bernardo do Campo, Brazil, 2023, pp. 627- 631, doi: 10.1109/INDUSCON58041.2023.10374674.
Gangrade, P. C. Sharma and A. K. Sharma, ”Colonoscopy Polyp Segmentation using Deep Residual U-Net with Bottleneck Attention Module,” 2023 Fifth International Conference on Electrical, Computer and Communication Technologies (ICECCT), Erode, India, 2023, pp. 1-6, doi: 10.1109/ICECCT56650.2023.10179818.
Ismail, B. Sziova, H. Taha and S. Nagy, ”The effect of different consequent setting on the effectiveness of Ko´czy-Hirota fuzzy rule interpolation in colonoscopy image classification,” 2022 57th Interna- tional Scientific Conference on Information, Communication and Energy Systems and Technologies (ICEST), Ohrid, North Macedonia, 2022, pp. 1-4, doi: 10.1109/ICEST55168.2022.9828693
Karthikha, D. Najumnissa and S. S. Rafiammal, ”Effect of U-Net Hyperparameter Optimisation in Polyp Segmentation from Colonoscopy Images,” 2022 Third International Conference on Intelligent Computing Instrumentation and Control Technologies (ICICICT), Kannur, India, 2022, pp. 1359-1364, doi: 10.1109/ICICICT54557.2022.9917700
Mohapatra, G. K. Pati and T. Swarnkar, ”Efficiency of Transfer Learn- ing for Abnormality Detection using Colonoscopy Images: A Critical Analysis,” 2022 IEEE Fourth International Conference on Advances in Electronics, Computers21 and Communications (ICAECC), Bengaluru, India, 2022, pp. 1-6, doi: 10.1109/ICAECC54045.2022.9716610.
Nam, S. -H. Park, N. S. Syazwany, Y. Jung, Y. -H. Im and S. -C. Lee, ”M3FPolypSegNet: Segmentation Network with Multi- Frequency Feature Fusion for Polyp Localization in Colonoscopy Images,” 2023 IEEE International Conference on Image Process- ing (ICIP), Kuala Lumpur, Malaysia, 2023, pp. 1530-1534, doi: 10.1109/ICIP49359.2023.10222864.
Ovi, N. Bashree, S. Ahmed, H. Nyeem and M. A. Wahed, ”Pixel Standardization Meets U2Net: Advancing Polyp Segmenta- tion in Colonoscopy Images,” 2023 International Conference on In- formation and Communication Technology for Sustainable Develop- ment (ICICT4SD), Dhaka, Bangladesh, 2023, pp. 448- 452, doi: 10.1109/ICICT4SD59951.2023.10303422.
Sierra-Jerez, J. Ruiz and F. Mart´ınez, ”A Non-Aligned Deep Repre- sentation to Enhance Standard Colonoscopy Observations from Vas- cular Narrow Band Polyp Patterns,” 2022 44th Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC), Glasgow, Scotland, United Kingdom, 2022, pp. 1671-1674, doi: 10.1109/EMBC48229.2022.9871752.
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