Deep Learning Applications in Medical Image Analysis

The unprecedented success of deep learning algorithms in image recognition coincides with a surge in the utilization of electronic medical records and diagnostic imaging. This review delves into the application of Deep learning algorithms in the realm of medical image analysis, with a particular focus on convolutional neural networks (CNNs), underscoring the clinical implications of these advancements. In an era characterized by the prolific generation of medical big data, Deep learning offers a distinct advantage, allowing for the automated discovery of intricate hierarchical relationships within the data without the need for painstaking manual feature engineering. This comprehensive exploration encompasses crucial research areas and applications in medical image analysis, spanning classification, localization, detection, segmentation, and registration, with a specific emphasis on diseases affecting the brain, liver, lungs, and blood. The discussion extends to the benefits of leveraging Deep learning in discerning patterns and features within diverse medical datasets, thereby enhancing diagnostic capabilities. The review concludes by addressing research challenges, highlighting emerging trends, and proposing potential future directions for the intersection of deep learning and medical image analysis. Overall, this synthesis provides a panoramic view of the evolving landscape where artificial intelligence augments clinical insights and transforms healthcare practices.

KEYWORDS: Convolutional Neural Networks (CNN’s), MobileNet.