A Transfer Learning-Based Deep Learning Model for Automated Breast Cancer Identification in Mammograms

Abstract Breast cancer is a severe health issue that affects women worldwide, underscoring the need for reliable and effective screening techniques. The early detection, diagnosis, and treatment of breast cancer are made possible by computer-aided diagnostic (CAD) systems that rely on mammograms. This study introduces a unique deep learning model that uses transfer learning to identify and categorize breast cancer automatically. Several recent studies have shown that deep convolutional neural networks (DCNNs) can be used to diagnose breast cancer in mammograms with performances comparable to or even superior to those of human experts. To extract attributes from the Mammographic Image Analysis Society (MIAS) dataset, the proposed model uses pretrained convolutional neural network (CNN) architectures such as ResNet50 and Visual Geometry Group networks (VGG)-16. This novel deep learning model holds significant potential for enhancing the efficiency and accuracy of breast cancer detection and classification. A preprint has previously been published 1