Abstract
This paper introduces automatic, reliable bone abnormality detection using Multi-view X-rays. The introduced method considers the seven extremity upper bones: shoulder, humerus, forearm, elbow, wrist, hand, and finger. For this purpose, three different CNN models are examined. The images are first enhanced by utilizing adaptive histogram equalization. After that, the enhanced multi-views images are fed into MobileNet [18], Inception [20], and DenseNet169 [21] CNN models to detect the bones’ abnormality. The majority voting is applied to obtain the final decision for multi-view X-rays of a bone. The average sensitivity achieved is 74.32%, 80.03%, and 85.79% and the average Specificity achieved is 92.66%, 86.99%, and 84.76% by utilizing MobileNet, Inception DenseNet169 models respectively. All the experiments were carried out using the MURA dataset.
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El-Saadawy, H., Tantawi, M., Shedeed, H.A., Tolba, M.F. (2021). Deep Learning Method for Bone Abnormality Detection Using Multi-view X-rays. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_5
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