基于卷积神经网络模型的深度学习骨质疏松骨折检测方法

R. Dhanalakshmi, M. Thenmozhi, Swati Saxena, Hemalatha Mahalingam
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摘要

骨质疏松症是一种骨骼疾病,发生的原因是最小的骨骼体质,破坏骨骼的微观结构,更多的是过度脆弱的断裂。全球主要的健身困难是骨质疏松症,尤其是老年人。它可能造成脊柱或髋部断裂,从而导致发病和负担。因此,早期诊断骨质疏松症和预测骨折的存在是非常必要的。然而,由于虚拟x线片的变化很小,因此对骨质疏松症的自动分析和诊断可能非常困难。在这项工作中提出的方法使用高维纹理函数表示从x线照片计算,以区分健康和骨质疏松问题。CNN帮助识别骨质疏松症使用骨结构MRI测量具有很高的准确性
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Convolutional Neural Network Model based Deep Learning Approach for Osteoporosis Fracture Detection
Osteoporosis is a bone ailment which takes place because of minimum bone physique, damaging of micro-structure of bone, more over an excessive vulnerability to breakage. The main fitness difficulty throughout the globe is Osteoporosis, particularly in aged people. It may create spinal or hip breakages which can result in morbidity and burden. Hence the diagnosis of Osteoporosis at early stages and forecasting the existence of the fracture is highly essential. However automated analysis and diagnosis of osteoporosis from virtual radiographs could be very difficult as they have little variations. The proposed approach in this work uses high-dimensional textured function representations calculated from radiography pictures to distinguish healthy from osteoporotic issues. CNN helps to identify osteoporosis using structural MRI measurements of bone with high accuracy
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