Enhancing Early Breast Cancer Detection Using Deep Learning Approach: A Comprehensive Review

Abstract

Breast cancer is still a major worldwide health concern that requires creative methods for early diagnosis and treatment. Deep learning-driven semantic segmentation algorithms have significantly improved recent advances in imaging biomarker extraction. By combining deep learning with conventional techniques, novel algorithms like the Deep Learning Assisted Efficient RNN Algorithm have greatly enhanced early detection showing an accuracy of 99.2%. The suggested semantic segmentation methodology provides better tumor region de-lineation accuracy than traditional techniques. Furthermore, deep learning-based systems have demonstrated efficacy and precision in the diagnosis of breast cancer, which may lower death rates. The main objectives of this study is to develop and show how deep learning can revolutionize breast cancer treatment by enhancing individualized care, the precision of diagnosis, and overall patient outcomes, which can result in more potent medical therapies. This work finds the best algorithm for promoting breast cancer research and delves deeper into deep learning for pan-cancer.