Histopathological Image Analysis Using Deep Learning Framework

Abstract

Breast cancer has the highest mortality rate. Therefore, histologic imaging evaluations must detect breast cancer early. Traditional methods are time-consuming and limit pathologists’ skills. Breast cancer histopathology picture segmentation is neglected by existing HIAs because of its complexity and lack of historical data with exact annotations. Histopathology breast cancer images are classified using graph-based segmentation. Graph-segmented images retrieve relevant features. Using recursive feature removal, breast cancer photographs are categorized. Breast cancer symptoms can be detected by appropriately classifying breast histopathology scans as abnormal or normal. Modern medicine diagnoses and predicts diseases, including cancer, using histopathological image analysis. Due to picture identification and feature extraction, deep learning can automate and improve histopathological image analysis. This study extensively analyses deep learning frameworks in histopathology image analysis. Starting with histopathological image interpretation’s challenges, this study emphasizes the intricate patterns, cell structures, and tissue anomalies that demand professional attention. It then examines CNNs, RNNs, and their variants’ design and ability to catch subtle features and patterns in histopathological images. We examine tumour detection, grading, segmentation, and prognosis using deep learning in histopathology. For each problem, this article evaluates cutting-edge deep learning models and approaches to demonstrate their accuracy and efficiency. While training deep learning models for histopathology image analysis, this study tackles data collection, preprocessing, and annotation. We also analyse automated clinical systems’ ethical and regulatory ramifications. Deep learning-based histopathological image processing case studies show patient care and applications. Multi-modal data fusion, transfer learning, and explainable AI may increase the accuracy and interpretability of histopathological image analyses.