Multi-class Classification of Colon and Lung Cancer using Deep Convolution Neural Network

Lung and colon cancers are among the most common forms of the disease. The key to effective treatment and long-term patient survival is a prompt and precise diagnosis. Recently, deep learning algorithms have demonstrated impressive results in classifying cancer types and enhancing diagnostic precision. In this research, a deep convolution neural method is proposed for classifying lung and colon cancer. Accuracy and precision values of 99.0% and 96.8%, respectively, were reached when the proposed model was evaluated using a dataset of lung and colon cancer images. The findings show that deep learning approaches have the potential to enhance cancer diagnostic efficiency and precision, leading to better health outcomes for patients.