Hybrid Deep Learning Approach for Automatic Detection in Musculoskeletal Radiographs

Biology

Gurpreet Singh¹  Darpan Anand¹  Gyanendra Prasad Joshi²  Kwang Chul Son³  Woong Cho⁴ 

[1] Department of Computer Science and Engineering, Chandigarh University, Mohali 140413, India;Department of Computer Science and Engineering, Sejong University, Seoul 05006, Korea;Department of Information Contents, Kwangwoon University, Seoul 01897, Korea;Department of Software Convergence, Daegu Catholic University, Gyeongsan 38430, Korea;

关键词: deep learning;    musculoskeletal abnormalities;    prediction;    convolutional neural network;    machine learning;    artificial intelligence;   

DOI  :  10.3390/biology11050665

来源: DOAJ

【 摘 要 】

The practice of Deep Convolution neural networks in the field of medicine has congregated immense success and significance in present situations. Previously, researchers have developed numerous models for detecting abnormalities in musculoskeletal radiographs of upper extremities, but did not succeed in achieving respectable accuracy in the case of finger radiographs. A novel deep neural network-based hybrid architecture named ComDNet-512 is proposed in this paper to efficiently detect the bone abnormalities in the musculoskeletal radiograph of a patient. ComDNet-512 comprises a three-phase pipeline structure: compression, training of the dense neural network, and progressive resizing. The ComDNet-512 hybrid model is trained with finger radiographs samples to make a binary prediction, i.e., normal or abnormal bones. The proposed model showed phenomenon outcomes when cross-validated on the testing samples of arthritis patients and gives many superior results when compared with state-of-the-art practices. The model is able to achieve an area under the ROC curve (AUC) equal to 0.894 (sensitivity = 0.941 and specificity = 0.847). The Precision, Recall, F1 Score, and Kappa values, recorded as 0.86, 0.94, 0.89, and 0.78, respectively, are better than any of the previous models’. With an increasing appearance of enormous cases of musculoskeletal conditions in people, deep learning-based computational solutions can play a big role in performing automated detections in the future.

【 授权许可】

Unknown