International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-8 Issue-12, October 2019 2429 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: L30111081219/2019©BEIESP DOI: 10.35940/ijitee.L3011.1081219 Abstract: In this paper, the Osteoarthritis (OA) analysis in knee radiographic images using artificial neural networks (ANN) is considered. In Osteoarthritis, mobility is restricted and bones rub each other causing extreme pain in knee due to cartilage disintegration. The cartilage destruction is minimal in the initial stage of OA. It is observed that a small number of researchers have implemented identification and grading of Osteoarthritis utilizing their own datasets for experimentation. However, there is still need of automatic computer aided techniques to detect Osteoarthritis for early recognition. In this work, a dataset of 1650 radiographic images of knee joints of OA patients are collected from different hospitals and have been annotated by two different orthopedic surgeons as per the Kellgren and Lawrence (KL) grading system. To automate this grading procedure, the local phase quantization and multi-block projection profile features are computed from the images and then presented to artificial neural network to classify the images based on the KL grading of the severity of the disease. The classification accuracy of 98.7% and 98.2% with reference to surgeon-1 and surgeon-2 opinions, respectively, is achieved. Keywords: Knee Radiography, Osteoarthritis (OA), Local phase quantization (LPQ), Multi-block Projection Profile (MB-PP), Artificial Neural Network. I. INTRODUCTION In a knee anatomy, cartilage holds important role in leg mobility. Cartilage is a bouncy material at the ends of the bones that helps easy movement and abides as a shock absorber. In Osteoarthritis (OA), cartilage is disintegrated because of which bones rub each other causing extreme pain and restricted mobility. The cartilage destruction is minimal if the disease is diagnosed in the initial stage and analyzed properly. The important clinical symptoms of OA in the initial stage are joint pain in knee, hip, ankle, spine etc. If any of these indications are experienced, the patients have to immediately consult the doctor/experts preferably Rheumatologists/Orthopedicians for diagnostic analysis. The experts examine the patient clinically and may recommend for a radiographic examination. Some of the important radiological parameters are cartilage disintegration, reduced joint space width, formation of osteophytes, loose bones and bone deformation [8]. Depending on the radiological Revised Manuscript Received on October 05, 2019. Shivanand S.Gornale, Professor, Department of Computer Science, School of Mathematics and Computing Sciences, Rani Channamma University, Belagavi, Karnataka, India. E-mail:shivanand_gornale@yahoo.com Pooja U.Patravali, Research Scholar, Department of Computer Science, School of Mathematics and Computing Sciences, Rani Channamma University, Belagavi, Karnataka, India. E-mail: pcdongare@gmail.com Prakash S.Hiremath, Professor, Dept. of Computer Science (MCA), KLE Technological University, Hubballi-580031, Karnataka, India Email:hiremathps53@gmail.com parameters and severity level, the individual joint is categorized into five different grades based on Kellgren and Lawrence (KL) grading system [15] depicted in Table 1. Table.1. Grading framework by Kellgren and Lawrence [15] KL Grades OA Analysis Grade0 (Normal OA) Radiographic parameters related to OA are absent Grade1 (Doubtful OA) Reduced joint space width Grade2 (Mild OA) Clear/ visible narrowing of joint space Grade3 (Moderate OA) Numerous bony outgrowths, sclerosis Grade4 (Severe OA) Massive bone spurs, extreme sclerosis, bone deformity Generally, analysis of X-ray images is done manually by the medical expert, which is time consuming, subjective and sometimes unpredictable. The complexities related to the medical images make it hard to examine them in an effective way. Thus, to overcome these difficulties, an automated method is proposed for early evaluation of Knee OA to protect the cartilage and other tissues from the damage and make the treatment more effective. The rest of the paper comprises related work, proposed methodology, experimental results and discussion along with the summary. II. RELATED WORK Radiological parameters like joint space width, sclerosis and osteophytes play very important role in assessment of Osteoarthritis [15]. Based on these parameters, the medical experts classify OA through manual inspection. From the literature, it is found that numerous researchers have utilized these radiological parameters for the detection of OA using various machine learning and computer vision techniques. Joseph Antony et al. [2] have used a method to localize knee joint and classify the joint OA using fully convolutional neural network and obtained better results. Jihye Lim et al. [3] have used deep learning neural network for OA detection using subjects‟ statistical and behavioral data. The result of 76.8% is achieved under the curve with scaled PCA. Yoo et al. [1] developed a scoring system to predict radiographic knee OA using KNHANES V-1 data and ANN. The results attained were helpful in early prediction of Knee OA. Lior Shamir et al. [4] have used WND-CHRM algorithm for the early detection of Knee OA using computer aided analysis. The classification rate of 91.5% for Moderate OA and 80.4% for Minimal OA was achieved. Shivanand Gornale et al. [9-14] have used contour based segmentation method to detect the cartilage region using Knee X-ray images [9-11]. Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network Shivanand S.Gornale, Pooja U. Patravali, Prakash S.Hiremath