Enhancing Diagnostic Accuracy in Radiology with Deep Learning Techniques
Abstract
This paper presents a deep learning framework designed to assist radiologists in diagnosing medical conditions from imaging data. We trained convolutional neural networks on a large dataset of radiological images to detect anomalies such as tumors and fractures. Our results indicate significant improvements in diagnostic accuracy and efficiency, showcasing the transformative impact of AI in radiology and emphasizing the need for integrating these technologies into clinical workflows.
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