Sift-BCD: SIFT-CNN integrated machine learning-based breast cancer detection

Globally, breast cancer (BC) has become one of the important reasons of death among women by emphasizing the necessity for early detection systems. Early detection is key to providing the best treatment outcomes and saving lives. Medical imaging techniques have been extensively used to diagnose and detect BC. However, manually diagnosing each image pattern requires a lot of time when using these techniques. To overcome this issue, a novel SIFT-CNN Integrated Fuzzy decision Tree based Breast Cancer Detection (SIFT-BCD) method is proposed for identifying BC cases in an early stage with minimal time. Initially, the mammogram images are taken from the CBIS-DDSM dataset to detect BC. The proposed SIFT-BCD method has three phases: pre-processing, segmentation, feature extraction, and classification. The mammogram images are given to the trilateral filter for eliminating the noisy distortions. The ROI based Unet is used to segment relevant areas in the noise-free images. The SIFT-CNN is utilized to retrieve the fine features from the mammogram images. The fuzzy decision tree is employed for classifying mammography images into three classes: malignant, benign, and normal. Specific metrics including accuracy, specificity, and sensitivity are used for evaluating the overall efficiency of the proposed SIFT-BCD method. The proposed SIFT-BCD achieves a better accuracy of 99.20% for identifying BC in their early stages. The proposed SIFT-BCD approach improves the overall accuracy of 2.7%, 0.87%, 3.5% and 0.92% better than Modified YOLOv5, IPBCS-DL, Modified AlexNet DCNN and BCCNN respectively.