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Computer-Aided Diagnosis of Knee Osteoarthritis From Radiographic Images Using Random Forest Classifier

Computer-Aided Diagnosis of Knee Osteoarthritis From Radiographic Images Using Random Forest Classifier
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Author(s): Pavithra D. (Avinashilingam Institute for Home Science and Higher Education for Women, India), Vanithamani R. (Avinashilingam Institute for Home Science and Higher Education for Women, India)and Judith Justin (Avinashilingam Institute for Home Science and Higher Education for Women, India)
Copyright: 2021
Pages: 17
Source title: Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics
Source Author(s)/Editor(s): Bhushan Patil (Independent Researcher, India)and Manisha Vohra (Independent Researcher, India)
DOI: 10.4018/978-1-7998-3053-5.ch019

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Abstract

Knee osteoarthritis (OA) is a degenerative joint disease that occurs due to wear down of cartilage. Early diagnosis has a pivotal role in providing effective treatment and in attenuating further effects. This chapter aims to grade the severity of knee OA into three classes, namely absence of OA, mild OA, and severe OA, from radiographic images. Pre-processing steps include CLAHE and anisotropic diffusion for contrast enhancement and noise reduction, respectively. Niblack thresholding algorithm is used to segment the cartilage region. GLCM features like contrast, correlation, energy, homogeneity, and cartilage features such as area, medial, and lateral thickness are extracted from the segmented region. These features are fed to random forest classifier to assess the severity of OA. Performance of random forest classifier is compared with ANFIS and Naïve Bayes classifier. The classifiers are trained with 120 images and tested with 45 images. Experimental results show that random forest classifier achieves a higher accuracy of 88.8% compared to ANFIS and Naïve Bayes classifier.

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