Differentiation of Soft Bone Strength in Human Femur Radiographic Images Using Sharpness Features and Extreme Learning Machine
نویسنده
چکیده
In this work, an attempt has been made to analyze human femur radiographic bone images using sharpness features and learning models. The sharpness features are derived for the neck of the femur bone images to characterize the trabecular structure. The significant parameters are found using Independent component analysis (ICA) and Principal Component Analysis (PCA). The first three most significant parameters are used as inputs to the Extreme Learning Machine (ELM) and Evolutionary Extreme Learning Machine (E-ELM) classifiers and the performance of the classifiers and feature selection techniques are analysed. The results demonstrate that it is possible to differentiate normal and abnormal images using sharpness features. Also, the E-ELM classifier using radial basis activation function performs better in terms of classification accuracy (99%) for the features selected using ICA Keywords— Evolutionary Extreme Learning Machine, Extreme Learning Machine, Human femur, Independent Component Analysis, Principal Component Analysis, Sharpness features, Trabecular structure.
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