Integration of Deep Learning and Image Processing for Colorectal Tumour Diagnosis

Abstract

Colorectal cancer is a major global health issue, accounting for a significant number of cancer cases and highlighting the importance of modern diagnostic methods for precise detection. A large set of histopathology photos was carefully gathered and thoroughly examined using strict quality control techniques for this study project. Reliable tumor classification relies on robust datasets from various sources, highlighting the need for careful dataset organization, accurate labeling, and removing poor-quality images to enhance model performance. Advanced image processing techniques can be strategically used to improve the reliability of convolutional neural network (CNN) data, especially through patch-based approaches and the application of Otsu's threshold methods. Incorporating fully connected layers, convolutional layers, and max-pooling into a cutting-edge CNN design significantly improves the ability to identify complex histology patterns accurately. The precise training and optimization methods used resulted in high accuracies of 99.69% and 99.32%, respectively. The Adam optimizer and eight-batch optimization technique are key to achieving these results. This new approach shows great promise as a useful tool for categorizing colorectal cancer in real-world situations, mainly due to its significant enhancements in accuracy and dependability.