Osteoarthritis Classification Algorithm Using CNN and Image Edge Detections | ||||
| JES. Journal of Engineering Sciences | ||||
| Article 7, Volume 53, Issue 1, January and February 2025, Page 102-117 PDF (569.55 K) | ||||
| Document Type: Research Paper | ||||
| DOI: 10.21608/jesaun.2024.266440.1306 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Hanafy Mahmoud Ali  
| ||||
| Computers and Systems Engineering Department, Faculty of Engineering, Minia University, El Minia, Egypt | ||||
| Abstract | ||||
| The paper presents a comprehensive computer-aided diagnosis (CAD) system designed for early detection of knee Osteoarthritis (OA) utilizing knee medical imaging and machine learning algorithms. Osteoarthritis is a prevalent chronic disease affecting various joints, primarily the fingers, thumbs, spine, hips, knees, and big toes, with secondary occurrences linked to pre-existing joint anomalies. Although more common among older individuals, OA can develop in adults of any age, characterized by degenerative changes in joints. Traditional diagnosis involves examining joint scans, typically through X-ray analyses being conducted by trained radiologists and orthopaedists, which can be time-consuming and subject to precision loss due to manual segmentation. Automatic segmentation and interpretation of joint X-ray scans are thus necessary to enhance clinical outcomes and bone calculation precision. The advent of deep learning technologies in medical systems has facilitated such transition, enabling efficient processing of large data volumes with improved accuracy. In particular, Convolutional Neural Networks (CNNs) being among the deep learning methods, have proven effectiveness in automating X-ray scan segmentation. The paper provides an overview of various deep learning and image processing techniques employed for automatic segmentation and interpretation of X-ray scans, facilitating disease diagnosis based on image data along with a proposed improved model of Visual Geometry Group VGG-16 with edge detection using X-ray images. A classification algorithm based on CNN and image edge detection is proposed demonstrating promising results, achieving predictive accuracies exceeding 90% across all suggested models. Particularly is the performance of the proposed VGG-16 after training with edge detection, which attained a training accuracy of 100% and a testing accuracy of 98.2%. This highlights the efficacy of deep learning approaches in enhancing diagnostic accuracy and efficiency in knee OA detection. A comparative evaluation of the proposed algorithm against other techniques based on performance metrics is reported. | ||||
| Keywords | ||||
| Osteoarthritis; CNN; X-ray; Deep Learning; Classification; feature extraction; knee osteoarthritis detection | ||||
| References | ||||
|
[1] K. Kamal, and H. Ez-Zahraouy A comparison between the VGG16, VGG19 and ResNet50 architecture frameworks for classification of normal and CLAHE processed medical images. Research Square; 2023. DOI: 10.21203/rs.3.rs-2863523/v1.
[2] Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553. Nature Publishing Group, pp. 436–444, May-2015.
[3] S. Glyn-Jones et al., “Osteoarthritis,” in the Lancet, 2015, vol. 386, no. 9991, pp. 376–387.
[4] Y. Zhang and J. M. Jordan, “Epidemiology of osteoarthritis,” Clinics in Geriatric Medicine, vol. 26, no. 3. Elsevier, pp. 355–369, 01-Aug-2010.
[5] R. Mahum, SU. Rehman, T. Meraj, HT. Rauf, A. Irtaza, AM. El-Sherbeeny MA, El-Meligy (2021) a novel hybrid approach based on deep CNN features to detect knee osteoarthritis. Sensors 21(18):6189
[6] M. D. Kohn, A. A. Sassoon, and N. D. Fernando, “Classifications in Brief: Kellgren-Lawrence Classification of Osteoarthritis,” Clin. Orthop. Relat. Res., vol. 474, no. 8, pp. 1886–1893, Aug. 2016.
[7] C. R. James, J. S. Dufek, and B. T. Bates, “Effects of injury proneness and task difficulty on joint kinetic variability,” Med. Sci. Sports Exerc., vol. 32, no. 11, pp. 1833–1844, Nov. 2000.
[8] A. K. Taneja et al., “MRI features of the anterolateral ligament of the knee,” Skeletal Radiol., vol. 44, no. 3, pp. 403–410, 2015.
[9] J. Andrew, M. DIvyavarshini, P. Barjo, and I. Tigga, “Spine Magnetic Resonance Image Segmentation Using Deep Learning Techniques,” in 2020 6th International Conference on Advanced Computing and Communication Systems, ICACCS 2020, 2020, pp. 945–950.
[10] J. Andrew, R. Fiona, and H. Caleb Andrew, “Comparative study of various deep convolutional neural networks in the early prediction of cancer,” in 2019 International Conference on Intelligent Computing and Control Systems, ICCS 2019, 2019, pp. 884–890.
[11] M. R. Karim et al., "DeepKneeExplainer: Explainable Knee Osteoarthritis Diagnosis from Radiographs and Magnetic Resonance Imaging," in IEEE Access, vol. 9, pp. 39757-39780, 2021, doi: 10.1109/ACCESS.2021.3062493.
[12] A. Tiulpin, J. Thevenot, E. Rahtu, P. Lehenkari, and S. Saarakkala, “Automatic knee osteoarthritis diagnosis from plain radiographs: A deep learning-based approach,” Sci. Rep., vol. 8, no. 1, pp. 1–10, Dec. 2018.
[13] D. D. Deokar and C. G. Patil, “Effective Feature Extraction Based Automatic Knee Osteoarthritis Detection and Classification using Neural Network.” 2015.
[14] P. Sharma and J. M. Singh, “A Novel Approach towards X-Ray Bone Image Segmentation using Discrete Step Algorithm,” undefined, 2013.
[15] M. Izadpanahkakhk, S. M. Razavi, M. Taghipour-Gorjikolaie, S. H. Zahiri, and A. Uncini, “Deep region of interest and feature extraction models for palmprint verification using convolutional neural networks transfer learning,” Appl. Sci., vol. 8, no. 7, Jul. 2018.
[16] A. Jamshidi, J. P. Pelletier, and J. Martel-Pelletier, “Machine-learning-based patient-specific prediction models for knee osteoarthritis,” Nature Reviews Rheumatology, vol. 15, no. 1. Nature Publishing Group, pp. 49–60, Jan-2019.
[17] J. Antony, K. McGuinness, N. E. O. Connor, and K. Moran, “Quantifying Radiographic Knee Osteoarthritis Severity using Deep Convolutional Neural Networks,” Proc. - Int. Conf. Pattern Recognit., vol. 0, pp. 1195–1200, Sep. 2016.
[18] G. W. Stachowiak, M. Wolski, T. Woloszynski, and P. Podsiadlo, “Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis,” Biosurface and Biotribology, vol. 2, no. 4, pp. 162–172, Dec. 2016.
[19] Y. Du, J. Shan, R. Almajalid, and M. Zhang, “Knee osteoarthritis severity level classification using whole knee cartilage damage index and ANN,” in Proceedings - 2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2018, 2019, pp. 19–21.
[20] S. S., P. U., and R. R., “Detection of Osteoarthritis using Knee X-Ray Image Analyses: A Machine Vision based Approach,” Int. J. Comput. Appl., vol. 145, no. 1, pp. 20–26, Jul. 2016.
[21] S. Boukir, O. Regniers, L. Guo, L. Bombrun and C. Germain, "Texture-based Forest cover classification using random forests and ensemble margin," 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy, 2015, pp. 3072-3075, doi: 10.1109/IGARSS.2015.7326465.
[21] Li, G., Li, S., Xie, J. et al. “Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices”. J Neuro-Engineering Rehabil 21, 45 (2024). https://doi.org/10.1186/s12984-024-01337-6
[22] K. S. Raju, V. Amudha and M. N, "Early Detection and Quantification of Osteoarthritis Severity in Knee Using Support Vector Machine with Improved Accuracy Compared to Convolutional Neural Network," 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Chennai, India, 2023, pp. 1-5, doi: 10.1109/ICONSTEM56934.2023.10142317.
[23] M. Tinhinane, N. Yassine, A. Ahmad, A. Soraya and J. Rachid, "Effects of Region of Interest Location on Osteoarthritis Detection Using Deep Feature Learning," 2023 Twelfth International Conference on Image Processing Theory, Tools and Applications (IPTA), Paris, France, 2023, pp. 1-6, doi: 10.1109/IPTA59101.2023.10319997.
[24] M. Karaköse, H. Yetış and M. Çeçen, "A New Approach for Effective Medical Deepfake Detection in Medical Images," in IEEE Access, vol. 12, pp. 52205-52214, 2024, doi: 10.1109/ACCESS.2024.3386644.
[25] N. P. Challa, B. Naseeba, G. Vyshnavi, T. Priyanka, N. Jajam and K. S. Prasanna, "Osteoarthritis Disease Detection using Efficient Hyper-Tuning Parameters," 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), Chennai, India, 2023, pp. 1-9, doi: 10.1109/ACCAI58221.2023.10200102
[26] R. Gautam and M. Sharma, “Prevalence and Diagnosis of Neurological Disorders Using Different Deep Learning Techniques: A Meta-Analysis,” J. Med. Syst., vol. 44, no. 2, pp. 1–24, Feb. 2020.
[27] R. Gautam, P. Kaur, and M. Sharma, “A comprehensive review on nature inspired computing algorithms for the diagnosis of chronic disorders in human beings,” Progress in Artificial Intelligence, vol. 8, no. 4. Springer, pp. 401–424, Dec-2019.
[28] P. Kaur and M. Sharma, “Diagnosis of Human Psychological Disorders using Supervised Learning and Nature-Inspired Computing Techniques: A Meta-Analysis,” Journal of Medical Systems, vol. 43, no. 7. Springer New York LLC, pp. 1–30, Jul-2019.
[29] E. Hosseini-Asl, R. Keynto, and A. El-Baz, “Alzheimer’s Disease Diagnostics by Adaptation of 3D Convolutional Network,” Proc. - Int. Conf. Image Process. ICIP, vol. 2016-Augus, pp. 126–130, Jul. 2016.
[30] Debnath, S., Roy, R. & Changder, S. A novel approach using deep convolutional neural network to classify the photographs based on leading line by fine-tuning the pre-trained VGG16 neural network. Multimed Tools Appl 83, 3189–3214 (2024). https://doi.org/10.1007/s11042-022-13338-5
[31] SR. Shah, S. Qadri, H. Bibi, SMW. Shah, MI. Sharif, F. Marinello, "Comparing Inception V3, VGG 16, VGG 19, CNN, and ResNet 50: A Case Study on Early Detection of Rice Disease". Agronomy. 2023; 13Rice. https://doi.org/10.3390/agronomy13061633
[32] S. Mohsen, A. M. Ali, E. -S. M. El-Rabaie, A. ElKaseer, S. G. Scholz and A. M. A. Hassan, "Brain Tumor Classification Using Hybrid Single Image Super-Resolution Technique With ResNext101_32× 8d and VGG19 Pre-Trained Models," in IEEE Access, vol. 11, pp. 55582-55595, 2023, doi: 10.1109/ACCESS.2023.3281529.
[33] A. Al-Amaren, M. O. Ahmed, and M. S. Swamy, " A low-complexity residual deep neural network for image edge detection," Applied Intelligence, Vol. 53, pp. 11282-11299, 2023 in IEEE Access, vol. 11, pp. 55582-55595, 2023.
[34] A. Benhagyousef, and A; Saidani, "Recent Advances on Image Edge Detection," Al-Amaren, M. O. Ahmed, M. S. Swamy, "A low-complexity residual deep neural network for image edge detection," published in an Edited Volume "Digital Image Processing - Latest Advances and Applications," editors F. Cuevas, P. Mazzeo and A. Bruno, DOI: 10.5772/intechopen.1003763, January 2024.
[35] M. Liu and P. Qian, "Automatic Segmentation and Enhancement of Latent Fingerprints Using Deep Nested UNets," in IEEE Transactions on Information Forensics and Security, vol. 16, pp. 1709-1719, 2021, doi: 10.1109/TIFS.2020.3039058.
[36] Dipmala Salunke, Pallavi Tekade, Nihar Ranjan, Deepali Ujalambkar, Sunil Sangve, Deepak Mane, "Real-Time Dimension Detection using Customized Canny Edge Detection Algorithm," International Journal of Engineering Trends and Technology, vol. 71, no. 9, pp. 375-384, 2023. Crossref, https://doi.org/10.14445/22315381/IJETT-V71I9P233
[37] P. Chen, "Knee osteoarthritis severity grading dataset", 2018.
[38] N. P. Challa, B. Naseeba, G. Vyshnavi, T. Priyanka, N. Jajam and K. S. Prasanna, "Osteoarthritis Disease Detection using Efficient Hyper-Tuning Parameters," 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI), Chennai, India, 2023, pp. 1-9, doi: 10.1109/ACCAI58221.2023.10200102.
[39] R. PV and U. Shanmugam, "Explainable AI for Medical Imaging: Advancing Transparency and Trust in Diagnostic Decision-Making," 2023 Innovations in Power and Advanced Computing Technologies (i-PACT), Kuala Lumpur, Malaysia, 2023, pp. 1-6, doi: 10.1109/i-PACT58649.2023.10434658.
[40] H. A. Alshamrani, M. Rashid, S. S. Alshamrani, A.H.D. Alshehri, "Osteo-NeT: An Automated System for Predicting Knee Osteoarthritis from X-ray Images Using Transfer-Learning-Based Neural Networks Approach". Healthcare, 11, 1206. https://doi.org/10.3390/healthcare11091206, 2023.
[41] Y. X. Teoh, A. Othmani, Kh. W. Lai, S. Li Goh, J. Usman, "Stratifying knee osteoarthritis features through multitask deep hybrid learning: Data from the osteoarthritis initiative”, Computer Methods and Programs in Biomedicine, Volume 242,2023,107807, ISSN 01692607, https://doi.org/10.1016/j.cmpb.2023.107807. (https://www.sciencedirect.com/science/article/pii/S016926072300473X).
[42] EA. Murphy, et al. Machine learning outperforms clinical experts in classification of hip fractures. Sci Rep. 2022 Feb 8; 12(1):2058. Doi: 10.1038/s41598-022-06018-9. PMID: 35136091; PMCID: PMC8825848. | ||||
|
Statistics Article View: 258 PDF Download: 266 |
||||
