Deep Learning-Based Models for Early Detection of Colon Cancer

This technical report presents a novel approach for the early detection of colorectal cancer (CRC) utilizing deep learning techniques, specifically focusing on feature extraction through discrete wavelet transform (DWT) and convolutional neural networks (CNN). The report outlines the significance of early diagnosis in improving patient outcomes and reducing treatment costs associated with late-stage CRC. The proposed model differentiates between normal and damaged tissues by employing DWT for feature extraction, which enhances classification accuracy and reduces training time. The model was evaluated on two datasets of colon images, demonstrating its effectiveness in accurately analyzing cancerous tissues. The findings indicate that the integration of DWT and CNN architectures results in superior classification performance compared to traditional methods. The report also discusses the importance of a multidisciplinary approach in CRC diagnosis and treatment, emphasizing the role of advanced imaging techniques and machine learning in improving diagnostic accuracy. Overall, this study contributes to the ongoing efforts to enhance CRC detection methodologies.