Developing a Machine Learning Model for Colon Cancer Detection from Colonoscopy Data

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.