Task-dependent Slicing with Convolutional Network Model for Histopathological Image Analysis

Abstract

Due to its high rates of both mortality and morbidity, cancer stands as the primary cause of death globally. The examination of histopathological images plays a crucial role in the early prediction of cancer, relying on manual practices for each individual affected. However, this phase is prone to errors, time-consuming, and doesn’t facilitate early-stage decision-making for pathologists. Despite significant advancements in computer-aided image processing, the analysis of histopathological morphology remains challenging due to the intricate nature of these images. Moreover, limited annotations restrict sample analysis. This work focuses on developing efficient deep-learning methods to analyze histopathological images by considering both global and local features. It involves the analysis of image patches and features to address issues related to image annotations. A unique task-specific slicing model, integrated with the convolutional network model (ts-CNN) is proposed. This framework aims to conduct patch-level feature examination and combine multiple samples to achieve the necessary outcomes for classification. The envisioned model aims to rectify inefficiencies present in various existing approaches. Using MATLAB 2020a, the proposed model was employed to classify cancer-based histopathological images,  demonstrating superior predictive performance. It assisted pathologists in early cancer prediction with an accuracy of 98.2 % and aided in decision-making processes, offering promising results.