Ensemble CNN Model for Computer-Aided Knee Osteoarthritis Diagnosis

Vikrant Bhateja, Yatndeep Dubey, Navaneet Maurya, Vikash Kumar Yadav, Sarika Shrivastava, Ahmad Taher Azar, Zeeshan Haider, Syed Umar Amin and Zafar Iqbal Khan
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Vikrant Bhateja: Veer Bahadur Singh Purvanchal University, India
Yatndeep Dubey: Veer Bahadur Singh Purvanchal University, India
Navaneet Maurya: Veer Bahadur Singh Purvanchal University, India
Vikash Kumar Yadav: Veer Bahadur Singh Purvanchal University, India
Sarika Shrivastava: Ashoka Institute of Technology and Management, India
Ahmad Taher Azar: Prince Sultan University, Saudi Arabia
Zeeshan Haider: Prince Sultan University, Saudi Arabia
Syed Umar Amin: Prince Sultan University, Saudi Arabia
Zafar Iqbal Khan: Prince Sultan University, Saudi Arabia

International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 2024, vol. 15, issue 1, 1-17

Abstract: Osteoarthritis (OA) is a multifaceted ailment posing challenges in its diagnosis and treatment due to the intricate nature of the disease. Particularly, Knee Osteoarthritis (KOA) significantly impacts the knee joint, manifesting through symptoms such as pain, stiffness, and limited movement. Despite its prevalence and debilitating effects, early detection of KOA remains elusive, often hindered by subjective diagnostic methods and the absence of reliable biomarkers. This research aims to address these challenges by leveraging deep learning techniques and ensemble methodologies for accurate KOA classification using knee X-ray images. This paper utilized a dataset sourced from the Osteoarthritis Initiative (OAI), comprising a large collection of knee X-ray images graded according to the Kellgren-Lawrence (KL) grading system. The proposed design methodology involves preprocessing the input X-ray images and training multiple pre-trained Convolutional Neural Network (CNN) models, including ResNet50, InceptionResNetV2, and Xception to classify KOA severity grades. Additionally, this work introduced an ensemble model by combining predictions from these base models to improve overall performance of the Computer-Aided Diagnosis (CAD) system. The obtained results demonstrate the effectiveness of the ensemble approach, outperforming individual algorithms in terms of accuracy, precision, recall, F1-score, and balanced accuracy. However, challenges persist in accurately distinguishing between adjacent KL grades, particularly grades#1 and #2, highlighting the need for further refinement. Notably, the proposed CAD model showcases superior predictive accuracy compared to various state-of-art methods, offering a promising avenue for early KOA diagnosis and personalized treatment strategies.

Date: 2024
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