An in-depth exploration of deep learning approaches for the prediction of breast cancer subtypes

Abstract

Author(s): Dr. Rajesh B Mapari

Breast cancer stands as a contemporary health crisis, inflicting a significant toll on women globally with its high mortality rate. Early detection and accurate classification are essential for effective treatment. However, attempts to comprehend the underlying causes of this cancer using conventional machine learning techniques encounter challenges, particularly in feature extraction. Conventional machine learning models are most effective when dealing with raw data based on extracted features. In response to this limitation, innovative deep learning techniques have been introduced to diagnose breast abnormalities through diverse imaging modalities, such as Mammogram, Magnetic Resonance Imaging (MRI), and Ultrasound, achieving remarkable levels of accuracy. This comprehensive survey delves into the obstacles faced by classical machine learning models and underscores the emergence of efficient predictive models enabled by cutting-edge deep learning methods. Within this review, we provide a comparative analysis of traditional machine learning approaches and the more advanced deep learning models

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