Artificial Intelligence Driven Colorectal Cancer Classification Via Deep Learning Technique

Collateral cancer is a serious concern for patients with primary tumors, as the development of secondary tumors can significantly reduce survival rates and increase the complexity of treatment. Colon polyps are a common precursor to colorectal cancer, and early detection is essential for effective treatment. Traditional strategies for polyp detection include colonoscopy and biopsy, which can be invasive and time-consuming. In this work, we explore the use of convolutional neural networks (CNNs) to detect polyps in colonoscopy images, as machine learning methods have shown potential in this area. Improving patient outcomes requires early detection and prevention of collateral cancer. However, precisely predicting the probability of recurrent tumors can be challenging due to the complexity of cancer progression and the multiplicity of factors that may influence tumor formation. In this study, we examine the use of a CNN to forecast the likelihood that patients with primary tumors may develop collateral cancer. Machine learning algorithms have demonstrated potential in predicting cancer outcomes based on patient data. The study uses a dataset of colonoscopy images, including both positive and negative cases of polyps. A CNN model is developed using this dataset to classify images as either positive or negative for polyps. The model is trained using a supervised learning approach, where the network learns from labeled examples of images with and without polyps. The accuracy of the CNN model is compared to other polyp detection strategies, such as traditional image analysis techniques and other machine learning algorithms.