Image Processing Techniques for Breast Cancer Detection

Abstract

Breast cancer, a malignancy originating from breast tissue, poses a significant health newlinethreat, particularly affecting women. Early detection is crucial for effective treatment, newlineand medical imaging plays a pivotal role in diagnosis. The research presents a newlinecomprehensive approach to breast cancer diagnosis, focusing on image processing newlineand deep learning techniques. The study employs two distinct datasets, namely the newlineBreast Cancer Cell Segmentation (BCCS) Dataset and the Breast Cancer Semantic newlineSegmentation (BCSS) Dataset, obtained through digital mammography. This work newlineconcentrates on the detection of the invasive ductal carcinoma (IDC) type of breast newlinecancer. newlineThe research begins by addressing the challenge of noise reduction in breast cancer newlineimagery through the application of spatial domain filters, including Median Filter newline(MF), Adaptive Median Filter (AMF), Progressive Switching Median Filter (PSMF), newlineand Relaxed Median Filter (RMF). Evaluation metrics such as Peak Signal to Noise newlineRatio (PSNR) demonstrate the superiority of RMF in noise reduction. newlineSubsequently, a novel deep learning framework for breast cancer segmentation is newline newline