Detecting Colon and Lung Cancer through Deep Learning CNN Model

Lung and colon cancers pose substantial global health challenges, being recognised as prevalent and potentially life-threatening tumours. This research focuses on the development and evaluation of a DL model that utilises Convolutional Neural Networks to classify images of lung and colon cancer into five distinct categories. By making use of a dataset consisting of 25,000 medical images, the CNN model was subjected to a rigorous 40-epoch training process with a batch size of 40. The noteworthy outcome of this investigation is the remarkable achievement of a 99% accuracy rate by the CNN model, showcasing the transformative potential of DL in medical image analysis. The implications of such high accuracy extend to early diagnosis, therapy planning, and overall patient care in the medical field. This paper underscores the versatility of CNN architecture in healthcare applications, emphasising its significance in addressing complex medical classification tasks. The importance of automation and accuracy in clinical settings is highlighted, urging further research and validation of deep learning methodologies. The study demonstrates the effectiveness of CNN models in cancer classification, thereby facilitating the advancement of medical image processing systems that are more precise and effective. The end result will be advantageous for healthcare practitioners and patients equally.