Normal and abnormal detection for knee osteoarthritis using machine learning techniques

Abstract

Osteoarthritis is the most broadly recognized disease in the knee joint that affects the cartilage, especially among the old age or overweight people. In the normal knee joint, the smooth and thin layer called cartilage covers the joint space of the bone and makes the joint smooth and prevents them from rubbing against one another, but can break, when the cartilage gets ruptured due to which bones start rubbing with one another, and this may cause severe pain, swelling and stiffness in the knee joint. The evaluation for osteoarthritis detection includes a clinical examination, and different medical imaging techniques are X-RAY images and MRI scans. There is developing method required for classification frameworks that can precisely distinguish and identify knee OA from plain radiographs. In this method we have examining the strategy of computer aided diagnosis for early identification of knee OA. Based on the procedure of x rays through computer image processing, segmentation, feature extraction and investigation by means of building a classifier, a viable computer aided detection method for knee was made to help specialists in their precise, convenient and identification of potential risk of OA. For this method a total of 126 knee x ray image were collected for assessing the knee OA. In this paper, we tried to diagnose about the normal or abnormal detection of cartilage depreciation. The HOG and DWT features are extracted from X-ray images of the knee joints. The extracted features are classified with two different machine learning classifiers, namely the SVM and ANN Patternet classifiers, and the results are demonstrated. The SVM classification is good when compared with ANN and provides a satisfactory accuracy rate of 85.33%. At last the classifier was superior both in time effectiveness and classification execution to the regularly utilized classifiers based on iterative learning. In this way it was suitable to utilize as a computer aided tool for the diagnosis of OA.