An Efficient USE-Net Deep Learning Model for Cancer Detection

Breast cancer (BrCa) is the most common disease in women worldwide. Classifying BrCa image extremely important for finding at an earlier stage and monitoring during treatment. The computer-aided detection methods have been used to interpret improve of screening treatment stages. However, if a new generated treatment, it will not classify correctly. main objective this research images newly images. model performs preprocessing, segmentation, feature extraction, classification. In hybrid median filtering (HMF) eliminate noise contrast enhanced using quadrant dynamic histogram equalization (QDHE). Then, ROI segmentation performed USE-Net deep learning model. CaffeNet extraction on segmented images, finally, classification made improved random forest (IRF) with extreme gradient boosting (XGB). obtained 97.87% accuracy, 98.45% sensitivity, 95.24% specificity, 98.96% precision, 98.70% f1-score ultrasound gives 98.31% 99.29% 90.20% 98.82% 99.05% mammogram