Transfer Learning for Accurate Classification of Breast Cancer in Medical Imaging

Transfer learning has recently been developed as a powerful technique for accurate classification of medical images. It is predominantly used in deep learning models to facilitate training of models on small data sets. It is based on the process of leveraging the knowledge gained from prior related tasks and transferring it to a new task. This technique can be used to improve the accuracy of classification models trained on medical images, specifically for the classification of breast cancer. Such models are able to provide an improved accuracy of cancer classification compared with those trained in a standard fashion. Additionally, transfer learning models demonstrate the ability to increase computational efficiency, reduce over fitting, and construct useful representations from data with fewer annotations. This technique is particularly useful for medical imaging due to the expense and difficulty in acquiring large annotated datasets for training purposes. This paper explores the use of transfer learning for accurate classification of breast cancer in medical imaging, and its potential applications in the diagnosis of this disease..