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).
Cite this paper
Bakshi, S. (2018). An Investigative Analysis on Mapping X-Ray to Live Using Convolution Neural Networks for Detection of Genu Valgum. Open Access Library Journal, 5, e5009. doi: http://dx.doi.org/10.4236/oalib.1105009.
Wolfe, S.A. (1991) Conservative Treatment of Genu Valgus and Varum with Medial/Lateral Heel Wedges. In-diana Medicine: The Journal of the Indiana State Medical Association, 84, 614-615.
Espandar, R., Mortazavi, S.M.-J. and Baghdadi, T. (2010) Angular Deformi-ties of the Lower Limb in Children. Asian Journal of Sports Medicine, 1, 46-53. https://doi.org/10.5812/asjsm.34871
White, G.R. and Mencio, G.A. (1995) Genu Valgum in Children: Diagnostic and Therapeutic Alternatives. JAAOS—Journal of the American Academy of Orthopaedic Surgeons, 3, 275-283.
Greene, W.B. (1994) Genu Varum and Genu Valgum in Children. In: Schafer, M., Ed., AAOS Instructional Course Lectures, American Academy of Orthopaedic Surgeons, Vol. 43, 151-159.
Eg-mont-Petersen, M., de Ridder, D. and Handels, H. (2002) Image Processing with Neural Networks—A Review. Pattern Recognition, 35, 2279-2301. https://doi.org/10.1016/S0031-3203(01)00178-9
Tomè, D., Monti, F., Baroffio, L., Bondi, L., Tagliasacchi, M. and Tubaro, S. (2016) Deep Convolutional Neural Networks for Pedestrian Detection. Signal Processing: Image Communication, 47, 482-489. https://doi.org/10.1016/j.image.2016.05.007
Esteva, A., Kuprel, B., Novoa, R.A., Ko, J., Swetter, S.M., Blau, H.M. and Thrun, S. (2017) Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks. Nature, 542, 115-118.
Sharma, K. (2014) Brain Tumor Detection Based on Machine Learning Algorithms. International Journal of Computer Applications, 103, 7-11. https://doi.org/10.5120/18036-6883
Tetko, I.V., Livingstone, D.J. and Luik, A.I. (1995) Neural Network Studies. 1. Comparison of Overfitting and Overtraining. Journal of Chemical Information and Computer Sciences, 35, 826-833.
Kingma, D.P. and Ba, J.L. (2015) Adam: A Method for Stochastic Optimization. International Conference on Learning Representations 2015. https://arxiv.org/abs/1412.6980
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