DOI: 10.7763/IJCTE.2025.V17.1383
Automated Diagnosis of Mitral Valve Diseases Using Echocardiographic Images: A Comparative Study of GLCM Features and Deep Learning Techniques
2. College of Engineering, University of Business and Technology, Jeddah, Saudi Arabia
3. Cyber Security Department, Faculty of Information Technology, Zarqa University, Zarqa, Jordan
4. Department of Computer Science, College of Information Technology, Misr University for Science and Technology (MUST), Giza, Egypt
5. Faculty of Engineering FEQS, INTI International University, Nilai, Malaysia
6. Faculty of Management, Shinawatra University, Pathum Thani, Thailand
7. Research Institute of Sciences and Engineering, University of Sharjah, United Arab Emirates
Email: hattar@zu.edu.jo (H.A.); jababneh@zu.edu.jo (J.A.); Oma.sarhan@must.edu.eg (O.A.S.); A.hamdy@must.edu.eg (A.H.); mohdahmed.hafez@newinti.edu.my (M.H.); mabdelhamid@sharjah.ac.ae (M.A.D.)
*Corresponding author
Manuscript received December 26, 2024; revised March 18, 2025; accepted May 12, 2025; published December 9, 2025
Abstract—Mitral Valve (MV) pathologies such as Mitral Valve Prolapses (MVP), Mitral Stenosis (MS), and type III regurgitation should be diagnosed as early as possible for the better management of the patients. This research work presents a method for the classification of MV diseases using echocardiographic images and texture analysis of the images using Gray Level Co-occurrence Matrix (GLCM) features in conjunction with Machine Learning (ML) classifiers. Initially, a Convolutional Neural Network (CNN) was employed to categorize echocardiographic images into two standard views: Apical Four-Chamber (A4C) and Parasternal Long-Axis (PLA). Next, the energy, contrast, correlation, and the entropy of GLCM-based texture features were obtained. The features were then fed into ML classifiers such as Random Forest (RF), Neural Networks (NN), Ensemble models to classify MV conditions. In the A4C view, the Neural Network Classifier (NNC) obtained an accuracy of 85% while in the Parasternal Long Axis (PLA) view, the accuracy was 84%. Some of the features of GLCM that were deemed important in the performance of the model were revealed. The results show that combining GLCM texture analysis with ML provides a potential way of improving the precision and reliability of MV disease diagnosis. The findings of this study contribute to advancements in cardiovascular disease detection by integrating machine learning techniques with echocardiographic analysis, ultimately supporting efforts to enhance public health and early disease diagnosis, in alignment with global healthcare initiatives.
Keywords—Gray Level Co-occurrence Matrix (GLCM), echocardiography, medical imaging, machine learning, mitral valve diseases
Cite: Hani Attar, Jafar Ababneh, Omar A. Sarhan, Ahmed Hamdy, Mohamed Hafez, and Mohanad A. Deif," Automated Diagnosis of Mitral Valve Diseases Using Echocardiographic Images: A Comparative Study of GLCM Features and Deep Learning Techniques," International Journal of Computer Theory and Engineering, vol. 17, no. 4, pp. 212-224, 2025.
Copyright © 2025 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).