A review of current imaging techniques for histopathology-based breast cancer diagnosis with comparative insights using machine learning and deep learning models and its challenges and future directions

Breast cancer remains one of the principal reasons of death among women worldwide, highlighting the critical requirement for primary and exact diagnosis to expand patient persistence rates. Manual clarification of histopathological slides is frequently subjective, time-consuming, and prone to inter-observer unpredictability. Recent developments in machine learning (ML) and deep learning (DL) techniques have renovated histopathological image analysis by qualifying automated, consistent, and reproducible diagnostic systems. This review analysis thoroughly observes research from 2018 to 2025, covering advanced methods for breast cancer detection based on histopathological images, with feature extraction, feature selection, and classification strategies, and medical image investigations. In this study compares existing ML methods, convolutional neural networks (CNN), vision transformers (ViT), graph convolutional networks (GCN), ensemble learning and hybrid frameworks across numerous standard and multiple benchmark datasets. Performance comparisons encompass filter-based, wrapper-based, hybrid, and ensemble learning models. In this study highlights the benefits of AI-driven diagnostic systems such as enhanced accuracy, scalability, and consistency, while addressing key challenges including data discrepancy, inadequate interpretability, and high computational cost. This comparative review highlights publicly accessible datasets, standard architectures, and emerging techniques in explainable artificial intelligence, ViT, and federated learning. Finally, potential directions for future research are drawn to guide researchers, clinicians, and developers in the direction of proceeding intelligent systems for breast cancer histopathological diagnosis.