Deep Learning for Medical Image Analysis

Medical image analysis aims to enhance medical outcomes by using images of internal body parts for purposes such as research and diagnosis. The field has been transformed by deep learning, which has achieved remarkable results in tasks such as image alignment, partitioning, feature extraction, and categorization. The main factor for this shift is the advances in computing power and the emergence of deep neural networks. Deep learning is adept at finding hidden patterns in medical images and helps doctors make more precise diagnoses. They have shown their usefulness in tasks such as organ categorization, cancer detection, disease categorization, and computer-aided diagnosis. Various deep learning methods have been developed to analyze medical images for different diagnoses. In this article, we review the application of state-of-the-art deep learning techniques in medical image processing. We first survey the clinical trials that use neural networks. We then review popular pre-training models and discuss different integration methods that can enhance the performance of the neural network. Finally, we measure the performance indicators for the deep learning model and present the results.