Detection of Osteoarthritis in Knee Radiographic Images using Artificial Neural Network
Shivanand S.Gornale1, Pooja U. Patravali2, Prakash S.Hiremath3
1Shivanand S.Gornale, Professor, Department of Computer Science, School of Mathematics and Computing Sciences, Rani Channamma University, Belagavi, Karnataka, India.
2Pooja U.Patravali, Research Scholar, Department of Computer Science, School of Mathematics and Computing Sciences, Rani Channamma University, Belagavi, Karnataka, India.
3Prakash S.Hiremath, Professor, Dept. of Computer Science (MCA), KLE Technological University, Hubballi, Karnataka, India.
Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 2429-2434 | Volume-8 Issue-12, October 2019. | Retrieval Number: L30111081219/2019©BEIESP | DOI: 10.35940/ijitee.L3011.1081219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
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.
Scope of the Article: Artificial Intelligence