Abstract
Breast cancer is one of the most common cancer types, and treatment largely depends on early detection. A person has a 13% (1 in 8 risks) of developing breast cancer at some time in their lives. Computer-aided diagnostic (CAD) systems powered by deep learning algorithms have enabled data analysis at high rates without compromising performance. Our work proposes an Enhanced EfficientNet (EEF-Net) for determining the severity of breast cancer through mammograms. EEF-Net is built on top of EfficientNet and has been fine-tuned to classify mammograms into three classes: benign, malignant, and healthy. The architecture was trained using the publicly accessible MIAS dataset, and a sophisticated image pre-processing pipeline was used to remove noise and other artifacts from the mammograms. The model achieved state-of-the-art results in the classification of breast cancer, achieving an accuracy of 97.14%, 98.67% sensitivity, 99.30% specificity, and 98.30% precision. EEF-Net will assist radiologists in mass screening patients with high precision and will minimize the radiologists’ workload.
Dishant Padalia
Computer Science Master’s Student at UMass Amherst
My research interests include computer vision, AI in healthcare and natural language processing.