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Frontiers in Biomedical Technologies، جلد ۱۰، شماره ۲، صفحات ۱۶۹-۱۷۹
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عنوان انگلیسی Designing an Intelligent Lesion Detection System Using Deep Architecture Neural Networks in the Lower Limb X-Ray Images
چکیده انگلیسی مقاله Purpose: Diagnosis of musculoskeletal abnormalities is essential due to more than 1.7 billion people worldwide being affected by musculoskeletal disorders. In this study, we focus on diagnosing musculoskeletal abnormalities in the lower extremities within X-ray images by deep architecture neural networks. Methods: Our dataset contains 61,098 musculoskeletal radiographic images, which includes 42658 normal images and 18440 abnormal images. Each image belongs to a single type of lower extremity radiography, including the toe, foot, ankle, leg, knee, femur, and hip joint. We proposed a new deep neural network architecture with two different scenarios that perform lower extremity lesion diagnosis functions with high accuracy. The core of the proposed method is a deep learning framework based on the Mask R-CNN and CNN. The model with the best results utilized the Mask R-CNN algorithm to generate the bounding box, followed by the CNN algorithm to detect the class based on that. Results: The proposed model can detect different types of lower limb lesions by an AUC-ROC of 0.925, with an operating point of 0.859 sensitivity and a specificity of 0.893. Conclusions: By comparing the different results, it can be concluded that the consecutive implementation of Mask R-CNN and CNN function better than Mask R-CNN and CNN separately.
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نویسندگان مقاله | Sepideh Amiri
b) Department of Computer Sciences, University of Copenhagen, Copenhagen, Denmark


| Mina Akbarabadi
Department of Information Technology, Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran


| Shahnaz Rimaz
d) Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran


| Fatemeh Abdolali
c) Department of radiology and diagnostic imaging, faculty of medicine and dentistry, Alberta University, Edmonton, AB, Canada


| Reza Ahadi
f) Department of anatomical science Iran University of medical science Tehran. Iran


| Mohsen Afshani
Department of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran


| Zahra alaei askarabad
Department of Radiation sciences, Allied medicine faculty, Iran University of Medical Sciences, Tehran, Iran


| Tahereh Kowsarirad
h) Department of Radiation sciences, Allied medicine faculty, Iran University of Medical Sciences, Tehran, Iran


| Sohrab Sakinehpour
Department of Radiation sciences, Allied medicine faculty, Iran University of Medical Sciences, Tehran, Iran


| Nazila Eyvazzadeh
Radiation Sciences Research Center, AJA University of medical sciences, Tehran, Iran


| Susan Cheraghi
Assistant Professor Faculty of Para-medicine, IUMS, Tehran, Iran
نشانی اینترنتی https://fbt.tums.ac.ir/index.php/fbt/article/view/449
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