Enhancing Pathology Insights: Deep Learning for Histopathological Image Analysis in Colorectal Cancer

Histopathological image analysis has emerged as a pivotal tool in the field of colorectal cancer diagnosis and prognosis. As the incidence of colorectal cancer continues to rise globally, the need for accurate, efficient, and scalable diagnostic methods becomes increasingly paramount. Deep Neural Networks (DNNs) have shown remarkable potential in various medical imaging tasks, including histopathological image analysis. In this paper, we introduce DNNI, a novel framework comprises of Deep Neural Networks and Inception, tailored specifically for the comprehensive analysis of colorectal cancer histopathological images. The DNNI framework leverages state-of-the-art deep learning techniques, drawing inspiration from the inception architecture, to address the unique challenges posed by colorectal cancer histopathology. Moreover, we present a large-scale dataset of colorectal cancer histopathological images, carefully curated and annotated, to facilitate training and evaluation of the DNNI model.This dataset incorporates diverse tissue samples, capturing various stages and subtypes of colorectal cancer, thus enabling robust and generalizable model development. Experimental results demonstrate the efficacy of DNNI as 98.25% in accurately identifying cancerous regions, grading tumor malignancy, and predicting patient outcomes. We compare our model's performance with existing approaches, showcasing superior accuracy and efficiency.