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Abstract
Coronary heart disease (CHD) is one of the most common causes of death and a significant health issue of concern globally. Recent cardiovascular epidemiological studies indicate that the worldwide cardiovascular diseases cause about 17.9 million deaths every year, with coronary heart disease constituting the biggest share of these mortalities. Timely coronary artery abnormalities identification is thus important in enhancing patient outcomes and minimizing mortality. Over the past years, AI and deep learning technologies have made great strides in enhancements to automated medical diagnosis systems, especially in medical imaging applications. Although the deep learning models have been successful in classifying images, individual deep neural network structures tend to lack the ability to be generalised when trained on heterogeneous medical data due to overfitting, model bias, and poorer generalisation. Ensemble learning has become an attractive method to enhance classification accuracy through multiple models and enhance their complementary advantages. This paper presents a deep learning method that uses the ensemble of images to detect coronary heart disease on coronary angiography images. The architecture is a combination of multiple convolutional neural networks models namely ResNet50, VGG16, and EfficientNetB0 to identify and extract meaningful features of the images. Their personal forecasts are then pooled by a majority voting process in order to come up with the ultimate classification output.
Evaluation metrics used in assessing the model will include accuracy, sensitivity, specificity, and precision as well as, the area under the ROC curve (ROC-AUC). The suggested solution is likely to provide superior diagnostic accuracy and general performance, in contrast to the conventional single-model solutions. The framework proposed will help cardiologists in the early diagnosis and clinical decision-making to enhance care delivery.
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References
Abdul, M., Ajmihachami, R., Ali, K., Alhameedawi, M., & Hasan, A. A. (2023). Using Ensembles and Machine Learning Techniques to Classify Heart Diagnosis. 2(3), 64–71.
Abimannan, S., El-Alfy, E. S. M., Chang, Y. S., Hussain, S., Shukla, S., & Satheesh, D. (2023). Ensemble Multifeatured Deep Learning Models and Applications: A Survey. IEEE Access, 11(September), 107194–107217. https://doi.org/10.1109/ACCESS.2023.3320042
Ahmed, S. F., Alam, M. S. Bin, Hassan, M., Rozbu, M. R., Ishtiak, T., Rafa, N., Mofijur, M., Shawkat Ali, A. B. M., & Gandomi, A. H. (2023). Deep learning modelling techniques: current progress, applications, advantages, and challenges. In Artificial Intelligence Review (Vol. 56, Issue 11). Springer Netherlands. https://doi.org/10.1007/s10462-023-10466-8
Alzubaidi, L., Bai, J., Al-Sabaawi, A., Santamaría, J., Albahri, A. S., Al-dabbagh, B. S. N., Fadhel, M. A., Manoufali, M., Zhang, J., Al-Timemy, A. H., Duan, Y., Abdullah, A., Farhan, L., Lu, Y., Gupta, A., Albu, F., Abbosh, A., & Gu, Y. (2023). A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications. Journal of Big Data, 10(1). https://doi.org/10.1186/s40537-023-00727-2
Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. In Journal of Big Data (Vol. 8, Issue 1). Springer International Publishing. https://doi.org/10.1186/s40537-021-00444-8
Kim, H. E., Cosa-linan, A., & Maros, M. E. (2021). A review of transfer learning for medical image analysis. July, 0–2.
Li, D., Xiong, G., Zeng, H., Zhou, Q., Jiang, J., & Guo, X. (2021). Machine learning-aided risk stratification system for the prediction of coronary artery disease. International Journal of Cardiology, 326, 30–34. https://doi.org/10.1016/j.ijcard.2020.09.070
Mohammed, A., & Kora, R. (2023). A comprehensive review on ensemble deep learning: Opportunities and challenges. Journal of King Saud University - Computer and Information Sciences, 35(2), 757–774. https://doi.org/10.1016/j.jksuci.2023.01.014
Roth, G. A., Mensah, G. A., Johnson, C. O., Addolorato, G., Ammirati, E., Baddour, L. M., Barengo, N. C., Beaton, A., Benjamin, E. J., Benziger, C. P., Bonny, A., Brauer, M., Brodmann, M., Cahill, T. J., Carapetis, J. R., Catapano, A. L., Chugh, S., Cooper, L. T., Coresh, J., … Fuster, V. (2020). Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update From the GBD 2019 Study. Journal of the American College of Cardiology, 76(25), 2982–3021. https://doi.org/10.1016/j.jacc.2020.11.010
Sagi, O., & Rokach, L. (2018). Ensemble learning : A survey. January, 1–18. https://doi.org/10.1002/widm.1249
Sharma, A., & Verbeke, W. J. M. I. (2020). Improving Diagnosis of Depression With XGBOOST Machine Learning Model and a Large Biomarkers Dutch. 3(April), 1–11. https://doi.org/10.3389/fdata.2020.00015
Sun, C., Liu, C., Wang, X., Liu, Y., & Zhao, S. (2024). Coronary Artery Disease Detection Based on a Novel Multi-Modal Deep-Coding Method Using ECG and PCG Signals. Sensors, 24(21). https://doi.org/10.3390/s24216939
Tan, M., & Le, Q. (2019). Rethinking model scaling for convolutional neural networks. Proceedings of the 36th International Conference on Machine Learning (ICML), 6105–6114.
Tang, P., Wang, Q., Ouyang, H., Yang, S., & Hua, P. (2023). The feasibility of early detecting coronary artery disease using deep learning-based algorithm based on electrocardiography Study population. 15(9), 3524–3537.
Tjoa, E., & Guan, C. (2021). A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI. IEEE Transactions on Neural Networks and Learning Systems, 32(11), 4793–4813. https://doi.org/10.1109/TNNLS.2020.3027314
Topol, E. J. (2019). High-performance medicine: the convergence of human and artificial intelligence. Nature Medicine, 25(1), 44–56. https://doi.org/10.1038/s41591-018-0300-7
Wang, J., Zhu, H., Wang, S. H., & Zhang, Y. D. (2021). A Review of Deep Learning on Medical Image Analysis. Mobile Networks and Applications, 26(1), 351–380. https://doi.org/10.1007/s11036-020-01672-7