AI-Assisted Breast Cancer Prediction, Classification, and Future Directions: A Narrative Review Involving Histopathological Image Datasets

Abstract

Breast cancer-related deaths in women have increased significantly in the past decade, emphasizing the need for an accurate and early diagnosis. AI-assisted diagnosis using deep learning and machine learning (DML) approaches has become a key method for analysing breast tissue and identifying tumour stages. DML algorithms are particularly effective for classifying breast cancer tissue images due to their ability to handle large datasets, work with unstructured data, generate automated features, and improve over time. However, the performance of these models is heavily on the datasets used for training, with the models performing inconsistently between different datasets. Given the prediction that by 2050, there will be more than 30 million new cancer cases and more than 10 million deaths worldwide, it is crucial to focus on recent advancements in DML algorithms and histopathological image datasets used in AI-assisted systems. Histopathological images provide critical information to identify tissue abnormalities, which directly impact model performance. This review discusses and analyses various DML-based models and the datasets used in their implementation, highlighting research gaps and offering suggestions for future improvements. The goal is to develop more effective and efficient approaches for the prediction of early-stage breast cancer. In addition, this early detection assists the healthcare professional in guiding prevention methods in smart healthcare systems.