International Journal of Information Technology and Computer Science (IJITCS)
IJITCS Vol. 17, No. 6, 8 Dec. 2025
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Optimized CNN for Cardiac Disease Detection Using Hybrid Crow Search and Dragonfly Algorithms
PDF (1584KB), PP.95-108
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Author(s)
B. Shamna
1,*
C. P. Maheswaran
2
A. Anitha
1
Index Terms
Adaptive Median Filter, ECG, Fuzzy C-Means, GLCM, Hybrid CS-DF Optimized CNN Classifier
Abstract
Cardiovascular Disease (CVD) is a hazardous condition for humans that is rapidly expanding around the globe in developed as well as developing nations, eventually resulting in death. In this disease, the heart often fails to supply sufficient oxygen to other parts of the body so that they are cannot able to perform their normal activities. It is critical to identify this problem immediately and precisely in order to save patients lives and avoid additional damage.Henceforth, this work proposes an efficient image processing strategy based on a hybrid algorithm optimised Convolutional Neural Network (CNN) classifier, which is used in this present research for precise identification of cardiac vascular disease.In the beginning, the Electrocardiogram (ECG) images are obtained and processed by removing noise using an adaptive median filter. The pre-processed ECG image is then divided into different regions using the Fuzzy C-Means (FCM) algorithm, which improves the accuracy of heart illness detection.Following segmentation, the Grey level Co-occurrence Matrix (GLCM) is employed to efficiently extract high-ranked features. Subsequently, the characteristics are considerably identified using a novel hybrid Crow Search Optimization (CSO) Dragon Fly Optimisation (DFO) algorithm-based CNN classifier for optimally categorising the cardiacvascular illness.The entire work is validated in Python software, and the results show that the novel method produces the best possible outcomes with a maximum precision of 98.12%.
Cite This Paper
B. Shamna, C. P. Maheswaran, A. Anitha, "Optimized CNN for Cardiac Disease Detection Using Hybrid Crow Search and Dragonfly Algorithms", International Journal of Information Technology and Computer Science(IJITCS), Vol.17, No.6, pp.95-108, 2025. DOI:10.5815/ijitcs.2025.06.05
Reference
[1]M. B. Abubaker, andB. Babayiğit, “Detection of cardiovascular diseases in ECG images using machine learning and deep learning methods,”IEEE Transactions on Artificial Intelligence,vol. 4, no. 2, pp. 373–382, 2022.
[2]A. H.Khan, M. Hussain, and M. K. Malik, “Cardiac disorder classification by electrocardiogram sensing using deep neural network,” Complexity, vol.2021, pp. 1–8, 2021.
[3]Z. Zhao, C. Liu, Y. Li, Y. Li, J. Wang, B.S. Lin, and J. Li, “Noise rejection for wearable ECGs using modified frequency slice wavelet transform and convolutional neural networks,” IEEE Access, vol.7, pp. 34060–34067, 2019.
[4]G. G.N.Geweid, and M. A. Abdallah, “A new automatic identification method of heart failure using improved support vector machine based on duality optimization technique,”IEEE Access, vol.7, pp. 149595–149611, 2019.
[5]P. Maji, H.K. Mondal, A.P. Roy, S. Poddar, and S.P. Mohanty, “iKardo: An Intelligent ECG device for automatic critical beat identification for smart healthcare,”IEEE Transactions on Consumer Electronics,vol. 67, no. 4, pp. 235–243, 2021.
[6]L. Mhamdi, O. Dammak, F. Cottin, and I.B. Dhaou, “Artificial intelligence for cardiac diseases diagnosis and prediction using ECG images on embedded systems,” Biomedicines, vol.10, no. 8, pp. 2013.
[7]Z. Ahmad, A. Tabassum, L. Guan, and N.M. Khan, “ECG heartbeat classification using multimodal fusion,” IEEE Access, vol.9 , pp. 100615–100626, 2021
[8]M.Baydoun, L. Safatly, O.K. Abou Hassan, H.Ghaziri, A. El Hajj, andH. Isma'eel, “High precision digitization of paper-based ECG records: a step toward machine learning,”IEEE journal of translational engineering in health and medicine, vol.7, pp. 1–8, 2019.
[9]G.A. Tadesse, T.Zhu, Y. Liu, Y. Zhou, J. Chen, M. Tian, andD. Clifton, “Cardiovascular disease diagnosis using cross-domain transfer learning,” 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2019.
[10]H. Li, X. Wang, C. Liu, Y. Wang, P. Li, H. Tang, L. Yao, and H. Zhang, “Dual-input neural network integrating feature extraction and deep learning for coronary artery disease detection using electrocardiogram and phonocardiogram,” IEEE Access,vol. 7, pp. 146457–146469, 2019.
[11]R.Banerjee, A.Ghose, and K. M. Mandana, “A hybrid CNN-LSTM architecture for detection of coronary artery disease from ECG,” 2020 International Joint Conference on Neural Networks (IJCNN). IEEE, 2020.
[12]K. Fatema, S. Montaha, M.A.H. Rony, S. Azam, M.Z. Hasan, andM. Jonkman, “A robust framework combining image processing and deep learning hybrid model to classify cardiovascular diseases using a limited number of paper-based complex ECG images,” Biomedicines, vol.10, no. 11, pp. 2835, 2022.
[13]S.Sathishkumar, and R. Devi Priya, “Efficient Classification of ECG Signals Using Probabilistic Neural Network in the Detection of Cardiovascular Diseases,” International Journal of Intelligent Systems and Applications in Engineering, vol.10, no. 3, pp. 247–255, 2022.
[14]T. Liu, Y. Tian, S.Zhao, X. Huang, and Q. Wang, “Residual convolutional neural network for cardiac image segmentation and heart disease diagnosis,” IEEE Access,vol. 8 , pp. 82153–82161, 2020.
[15]S. S. Sarmah, “An efficient IoT-based patient monitoring and heart disease prediction system using deep learning modified neural networks,”IEEEAccess, vol.8, pp. 135784–135797, 2020.
[16]S. B.Shuvo, S. N. Ali,S. I. Swapnil,M. S.Al-Rakhami, and A. Gumaei, “CardioXNet: A novel lightweight deep learning framework for cardiovascular disease classification using heart sound recordings,” IEEEAccess, vol.9, pp. 36955–36967, 2021.
[17]N. Khateeb, and M. Usman, “Efficient heart disease prediction system using K-nearest neighbor classification technique,”Proceedings of the international conference on big data and internet of thing (BDIOT), pp. 21–26, 2017.