Classification of Osteoporosis from Phalanges Computed Radiography Images Based on Convolutional Neural Network
Classification of Osteoporosis from Phalanges Computed Radiography Images Based on Convolutional Neural Network
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基于卷积神经网络的指骨计算机X线图像骨质疏松症分类
DOI：
10.11318/mii.36.72
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
青木 隆敏
中科院分区：
文献类型：
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作者：
畠野 和裕;村上 誠一;植村 知規;陸 慧敏;金 亨燮;青木 隆敏
骨质疏松症被认为是骨的主要疾病之一。虽然骨质疏松症的影像诊断是有效的，但人们担心与诊断成像相关的放射科医生的负担增加，由于经验差异导致的诊断结果不一致，以及未发现的病变。因此，在这项研究中，我们提出了一个诊断支持方法来分类骨质疏松症的指骨计算机X线摄影图像和分类结果呈现给医生。在所提出的方法中，我们使用卷积神经网络构建分类器，并分类正常情况下和异常情况下的骨质疏松症。在我们的实验中，使用从101例CR图像生成的输入图像构建了两种CNN模型，并使用受试者工作特征（ROC）曲线上的曲线下面积（AUC）值进行评估。最终获得的AUC为0.995。
Osteoporosis is known as one of the main diseases of bone. Although image diagnosis for osteoporosis is effective, there are concerns about increased burden of radiologists associated with diagnostic imaging, uneven diagnostic results due to experience difference, and undetected lesions. Therefore, in this study, we propose a diagnosis supporting method for classifying osteoporosis from phalanges computed radiography images and presenting classification results to physicians. In the proposed method, we construct classifiers using convolution neural network and classify normal cases and abnormal cases about osteoporosis. In our experiments, two kinds of CNN models were constructed using input images generated from 101 cases of CR images and evaluated using Area Under the Curve (AUC) value on Receiver Operating Characteristics (ROC) curve. Finaly, AUC of 0.995 was obtained.