Fracture Detection in Radiology with Resnet-152 & CNN

Abstract

In the field of medicine, fracture identification in medical imaging is a crucial task that has significant effects on patient diagnosis and therapy. Deep learning methods have demonstrated amazing promise in improving fracture diagnosis efficiency and accuracy in recent years. This survey paper offers a thorough examination of the most recent developments, difficulties, and breakthroughs in deep learning-based fracture identification. We explore the key concepts of deep learning and how it applies to many types of medical imaging, including MRIs, CT scans, and X-rays. This survey covers widely used deep learning concepts including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and more recent developments like DenseNet and ResNet.Our goal is to give researchers, medical professionals, and policymakers a thorough grasp of the state of deep learning-based fracture diagnosis by analyzing a wide spectrum of recent publications. The authors highlight areas of prospective future research as well as how these developments might affect clinical practice. Will also discuss about the benefits deep learning brings to this field. In conclusion, the authors will discuss the future directions of this technology.

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