EFFICIENT SOFTWARE IMPLEMENTATION OF DEEP NEURAL NETWORKS FOR COMPUTER VISION TASKS

Abstract

The remarkable success of deep learning algorithms in image recognition coincides with a substantial increase in the use of electronic medical records and diagnostic imaging. This review explores the application of deep learning algorithms in the field of medical image analysis, specifically emphasizing convolutional neural networks (CNNs) and highlighting the clinical implications of these advancements. In an era characterized by the abundant generation of medical big data, deep learning offers a distinct advantage by enabling the automated discovery of intricate hierarchical relationships within the data, eliminating the need for labor-intensive manual feature engineering. This comprehensive examination covers essential research areas and applications in medical image analysis, including classification, localization, detection, segmentation, and registration, with a specific emphasis on diseases affecting the brain, liver, lungs, and blood. The discussion also extends to the advantages of utilizing deep learning to identify patterns and features within diverse medical datasets, thereby improving diagnostic capabilities. The review concludes by addressing research challenges, highlighting emerging trends, and suggesting potential future directions for the integration of deep learning and medical image analysis. In essence, this synthesis offers a comprehensive perspective on the evolving landscape where artificial intelligence augments clinical insights and revolutionizes healthcare practices.