DEEP LEARNING APPLICATIONS IN MEDICAL IMAGING: ENHANCING DIAGNOSTIC ACCURACY

Abstract

The rapid advancements in deep learning technologies have significantly impacted the field of medical imaging, offering unprecedented improvements in diagnostic accuracy, efficiency, and early disease detection. This paper explores the applications of deep learning in medical imaging, focusing on its ability to analyze medical images such as X-rays, MRIs, CT scans, and ultrasounds. The integration of convolutional neural networks (CNNs), deep neural networks (DNNs), and other deep learning architectures has revolutionized the analysis of imaging data, enabling more precise detection of anomalies, abnormalities, and diseases like cancer, cardiovascular conditions, and neurological disorders. The study also discusses the challenges associated with the implementation of deep learning models in clinical settings, such as data quality, interpretability, and computational requirements. Finally, we explore future directions and the potential for deep learning to further enhance medical imaging's role in improving patient outcomes.

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