Deep Learning Methods for Breast Cancer Classification and Segmentation Using MRI Scans: A Review

Worldwide, breast cancer affected women increasingly with its incidence influenced by a complex interplay of genetic, environmental, and lifestyle factors, resulting in mortalities and ruined lives after getting affected by this malicious disease especially in younger ages. At that point, researchers have developed tools to treat this disease and continued to enhance their tools to reduce the number of mortalities using imaging tools like mammography, x-rays, magnetic resonance imaging and more. They indicated that when it is earlier diagnosing breast cancer it is easier to handle way too better in a try to achieve their goal improving survival rates. This review provides to focus on recent peer-reviewed research within the last decade that used deep learning methods like convolutional neural networks for breast cancer prediction/classification or segmentation using magnetic resonance imaging scans, that’s due its ability to locate lesions/malignancies that usually escapes traditional imaging tools. By evaluating models’ architectures, datasets, preprocessing for each study, key findings of them revealed that using such deep learning techniques have demonstrated truly promising results achieving high performance metrics for breast cancer assessment. While several limitations still exist like data availability, data quality, and data generalizability. Having that in hands, this review assured the importance of keeping developing robust, interpretable and clinically applicable AI models using MRIs to aid radiologists eliminate tedious tasks and support them with decision-making process.