Breast cancer detection with enhanced automated noise filter using hybrid shallow GAN
Shiva Priya.D, V. Radha · IET conference proceedings. · 2023
Breast cancer has become common among women and leads to both critical issues and life-threatening problems due to repeated radiations, changes in life style, hormonal and heredity reasons. The probability of facing the breast cancer is nearly one out of six, and hence, detection of cancer in the early stages will help to diagnose the disease and improve the survival rate. A Novel architecture using Hybrid Shallow GAN (HSGAN) is modeled here to detect the mammogram Images with better accuracy. The proposed work with HSGAN model is compared with state-of-the-art approaches with a competent accuracy of 96.21%. Moreover, to simplify the diagnosis, a prognostic approach is modeled taking the statistical parameters such as mean, standard deviation, kurtosis, contrast and heterogeneity of the given data into consideration, thereby improving decision process. The proposed method has the potential to be used in clinical practice to improve the accuracy of breast cancer detection and reduce false positives and false negatives.