Deep Learning-Based Computer-Aided Diagnosis Model for the Identification and Classification of Mammography Images

Cancer of the breast is an illness that has the potential to be fatal for females all over the world. Even with the advancements that have been made in treatment, breast cancer cannot be prevented or cured; however, with early identification, one's life expectancy can be increased. A woman's overall health can be improved, which can add years to her life expectancy, if breast cancer is detected at an earlier stage. Radiological screening is a well-known method that is utilised for cancer prevention and detection in significant amounts. Mammograms have the ability to detect breast cancer as well as tumours that may be present in the breast. Recent study has demonstrated that DL-based CAD models can assist radiologists in establishing automated diagnosis of breast cancer in patients. The DL-based CAD model helps radiologists diagnose breast cancer automatically, according to recent research. DL techniques utilising convolutional neural network have gained interest because to their effectiveness in automating data feature representation and maximising accuracy by merging classification and feature representations. It successfully diagnoses clinical pictures. The research aims to build DL-based breast cancer diagnosis models and to review state-of-the-art ML and DL models for breast cancer diagnosis and classification. The research also examines the performance of the proposed models on the benchmark dataset. Sensitivity, specificity, accuracy, and F -measure measure performance. The experimental results showed that the proposed models are effective compared to modern methods. The proposed models are effective for breast cancer diagnosis and categorization.