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Shin, H.C., Roth, H.R., Gao, M., Lu, L., Xu, Z., Nogues, I., Summers, R.M., et al. (2016) Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Transactions on Medical Imaging, 35, 1285-1298.
https://doi.org/10.1109/TMI.2016.2528162

has been cited by the following article:

• TITLE: An Investigative Analysis on Mapping X-Ray to Live Using Convolution Neural Networks for Detection of Genu Valgum

  AUTHORS: Satyake Bakshi

  KEYWORDS: Recommendation Systems, Artificial Neural Networks, Convolutional Neural Networks, Biomechanics, Knock Knees, Genu Valgum, Informatics

  JOURNAL NAME: Open Access Library Journal, Vol.5 No.11, November 23, 2018

  ABSTRACT: Introduction: Bow Legs and Knock Knees are quite common in growing children, which usually affect the lower portions of the body, however such disorders usually do not have any pathological significance. In this paper, we investigate a method using deep learning to correctly draw a boundary between a physiologically normal knee and a genu valgum. Objective: To draw a decision boundary between what is classified as Normal and what is “Abnormal” i.e. a knee exhibiting features of Knock knees which is Genu Valgum by using AI and ML tools. Methods: For this study the Adam Gradient descent was used which is a combination of AdaGrad and RMSProp. There is also an implementation of grid search for “self-selection” of parameters by the neural network which is the unique point that most existing ML algorithms on account of self-learning capability much like un-supervised learning but limited to parameter selection. In the second part, we try to investigate the outcome using X-ray version of the disorder and try to compare if the result is truthful in accordance to the patient’s case. Results: The two types of Knees had been correctly classified up to an accuracy of 89% to 90% (by using normal to normal) which is really good for most physicians or sports instructors to use as an initial screening tool for most athletes/patients. However, the second part shows interesting results with an accuracy of 60% (X-ray to Normal).