A Deep Learning Approach for Multi-Class Histopathological Classification of Colorectal Cancer Tissues

Abstract

Colorectal cancer (CRC) is the third most common cancer worldwide and the second leading cause of cancer-related deaths. Early diagnosis is critical for effective treatment and better patient outcomes, but the manual diagnosis process is time-consuming, inefficient, and prone to bias. This research proposes a deep learning-based automated system for the classification and diagnosis of CRC using histopathological images obtained from biopsy samples. The study utilizes advanced deep learning architectures, including EfficientNet-B4, to classify CRC tissues into eight categories: TUMOR, STROMA, COMPLEX, LYMPHO, DEBRIS, MUCOSA, ADIPOSE, and EMPTY. By focusing on biopsy images, which are crucial for accurate diagnosis but often underutilized compared to imaging modalities like CT scans and MRIs, the study aims to address gaps in the accuracy and efficiency of existing methods. Our approach demonstrates superior classification accuracy, particularly in differentiating challenging tissue classes such as STROMA and COMPLEX. The results show that the deep learning model not only automates the diagnosis process but also reduces the potential for human error, offering a promising tool for pathologists in diagnosing colorectal cancer at an early stage. This study contributes to advancing the application of AI in cancer diagnosis by enhancing the accuracy and efficiency of tissue classification from histopathological images.