Advancements in Breast Cancer Detection: A Comprehensive Review of Deep Learning Techniques
Drishti Arora, Shruti Gupta, Jashekam Singh Chawla, Rakesh Kumar Garg · 2023
This paper addresses the pressing issue of breast cancer-related mortality by comprehensively analyzing six distinct deep learning models utilized in the past decade. With a focus on timely and precise diagnosis, the study evaluates the performance metrics of each model across diverse datasets, image modalities, and methodologies. The primary goal is to identify the most efficient and precise deep learning architecture tailored to classify breast cancer tumors, with potential implications for optimizing early detection strategies and advancing clinical decision-making processes. The findings hold significant implications for refining clinical decision-making processes, potentially leading to more efficacy in timely detection strategies. This research emphasizes on the broader field of image analysis and its role in combating breast cancer.