Deep Learning Frameworks for Histopathological Image Processing in Colorectal Cancer Diagnostics

Abstract Artificial intelligence (AI) in the field of pathology and medical diagnostics has rapidly evolved, with applications focused on analyzing histopathological images. These images, obtained from biopsies or surgical procedures, are crucial for diagnosing diseases, particularly cancer, and provide essential insights into tissue structure. Traditionally, pathologists faced a challenging task in accurately analyzing the cellular features within these images. However, the introduction of deep learning, has significantly improved diagnostic reliability. In this study, two convolutional neural network models were implemented and compared. Both were trained on a multiclass dataset of histological images related to colorectal cancer. Among the two tested models, VGG-19 demonstrated the best performance, achieving a precision rate of 98%. Using a Python-based graphical interface, we employed the "Grad-CAM" technique to gain deeper insight into the model's classification process and identify key regions during training. The application of artificial intelligence to colon histopathological images also has the potential to improve patient outcomes and early cancer diagnosis. By combining AI with intuitive interfaces, we can enhance diagnostic accuracy and accelerate the analysis process.