DEVELOPING A DEEP LEARNING MODEL WITH TRANSFER LEARNING FOR BREAST CANCER DETECTION AND CLASSIFICATION USING MAMMOGRAPHY IMAGES

Breast cancer affects people all around the world and is a major cause of death for women. For the disease to be prevented and managed, early discovery is essential. Although there are other ways to identify breast cancer, mammography has shown to be a very successful strategy. The need to develop deep learning architectures and use transfer learning to increase detection performance has been brought to light by current research on the use of artificial intelligence (AI) and deep learning in breast cancer diagnosis. In this research, we offer a deep learning architecture that uses mammography imagery to autonomously diagnose breast cancer. Our framework utilizes a modified DenseNet-121 model and transfer learning, resulting in the Intelligent Learning Based Breast Cancer Detection (ILB-BCD) algorithm. An empirical study with a benchmark dataset, known as CBIS-DDSM, demonstrates that our proposed model surpasses numerous existing deep learning models with an impressive 99.16% accuracy. The success of our deep learning framework suggests its potential integration into existing healthcare systems to create an automated clinical decision support system (CDSS) for the detection of breast cancer.