Adopting Image Classification Using Pretrained Models with DCNN to Segment an Image in General 2-D Echo Cardiograph Images
Keywords:
Electrocardiogram, Arrhythmia monitors patient health 2D Echo classification guided learningAbstract
The biggest cause of death worldwide is cardiovascular disease. Heart attacks and strokes can be avoided with early diagnosis and knowledge of the disease's trajectory. Early diagnosis of cardiac irregularities is made possible with the help of electrocardiogram (2D Echo) signals, which non-invasively monitor heart activity. Massive amounts of 2D Echo data can be difficult and error-prone to manually examine, which is why researchers are looking into automated interpretation methods to help clinicians make quick and correct choices. In order to monitor patients with cardiac disease and enable early detection and arrhythmia categorization, smart healthcare equipment are essential. However, because of non-linearity and weak amplification, categorizing 2D Echo recordings is difficult. The performance of traditional machine-learning (ML) classifiers when processing large-dimensional data with poorly modelled relationships is questionable. In order to address the limitations of ML classifiers, this study suggests an automated approach combining ResNet18, SegNet, Mobile-Net segmentation, and Feature extraction approaches with Dynamic CNN (D-CNN) classification. Tests using Stanford University's echo net-dynamic dataset show a considerable improvement in classifier performance, outperforming more sophisticated techniques with over 99.92% accuracy and 99.81% sensitivity. This strategy has the potential to help medical practitioners make quicker and more precise therapy decisions for individuals with heart disease.
Downloads
References
Lakshminarayan K, Anderson DC, Herzog CA, Qureshi AI. Clinical epidemiology of atrial fibrillation and related cerebrovascular events in the United States. Neurologist 2008;14:143-50.
Parekh DH, Dahiya V. Predicting breast cancer using machine learning classifiers and enhancing the output by combining the predictions to generate optimal F1-score. Biomed Biotechnol Res J (BBRJ)2021;5:331.
Sabeenian RS, Vijitha V. Identification and categorization of brain tumors using ensemble classifiers with hybrid features. BiomeBiotechnol Res J (BBRJ) 2021;5:357.
Anand R, Sowmya V, Menon V, Gopalakrishnan A, Soman KP. Modified VGG deep learning architecture for COVID 19 classification using chest radiography images. Biomed Biotechnol Res J (BBRJ)2021;5:43-9.
Rezayi S, Ghazisaeedi M, Kalhori SR, Saeedi S. Artificial intelligence approaches on X-ray-oriented images process for early detection of COVID-19. J Med Signals Sens 2022;12:233-53.
Moody GB, Mark RG. The impact of the Tanford dataset. IEEE Eng Med Biol Mag 2001;20:45-50.
Yilmaz EÇ, Ozdemir S. Baby crying analyzing and solution using matlab graphical user interface; interdisciplinary collaboration between engineering and nursing. Biomed Biotechnol Res J (BBRJ) 2022;6:410
Pal A, Srivastva R, Singh YN. CardioNet: An efficient 2D Echo arrhythmia classification system using transfer learning. Big Data Res 2021;26:100271.
Merdjanovska E, AleksandraR. Comprehensive survey of computational 2D Echo analysis: Databases, methods and applications. Expert Syst Appl 2022;203:117206.
Hong S, Zhou Y, Shang J, Xiao C, Sun J. Opportunities and challenges of deep learning methods for electrocardiogram data: A systematic review. Comput Biol Med 2020;122:103801.
Jang JH, Kim TY, Yoon D. Effectiveness of transfer learning for deep learning-based electrocardiogram analysis. Healthc Inform Res 2021;27:19-28.
Singh P, Sharma A. “Atrial Fibrillation Classification Using Transfer Learning.” In 2021 IEEE Bombay Section Signature Conference (IBC), IEEE; 2021. p. 1-5.
Cao M, Zhao T, Li Y, Zhang W, Benharash P, Ramezani R. 2D Echo heartbeat classification using deep transfer learning with convolutional neural network and STFT technique 10.48550/arXiv.2022;2206.14200.
Salem M, Taheri S, Yuan JS. “2D Echo Arrhythmia Classification Using Transfer Learning from 2-Dimensional Deep CNN Features.” In 2018 IEEE Biomedical Circuits and Systems Conference (BioCAS), IEEE; 2018. p. 1-4.
Asif RN, Abbas S, Khan MA, Sultan K, Mahmud M, Mosavi A. Development and Validation of Embedded Device for Electrocardiogram Arrhythmia Empowered with Transfer Learning. Computational Intelligence and Neuroscience, vol. 2022, Article ID 5054641, 15 pages,2022. https://doi.org/10.1155/2022/5054641.
Chen L, Xu G, Zhang S, Kuang J, Hao L. Transfer learning for electrocardiogram classification under small dataset. In: Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting. Cham: Springer;2019. p. 45-54.
Weimann K, Conrad TO. Transfer learning for 2D Echo classification. Sci Rep 2021;11:5251.
Kent M, Vasconcelos L, Ansari S, Ghanbari H, Nenadic I. Transfer learning application of a novel frequency shift convolutional neural network method for atrial fibrillation classification. Europace 2022;24 Suppl 1:pp.euac053-017, https://doi.org/10.1093/europace/euac053.017.
Ghaffari A, Madani N. Atrial fibrillation identification based on a deep transfer learning approach. Biomed Phys Eng Express 2019;5:035015.
[20]. Ullah H, Bu Y, Pan T, Gao M, Islam S, Lin Y, et al. “Cardiac Arrhythmia Recognition Using Transfer Learning with a Pre-trained DenseNet.” In 2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML), IEEE; 2021. p. 347-53.
Gaddam PG, Sreehari RV. Automatic classification of cardiac arrhythmias based on 2D Echo signals using transferred DL convolution neural network. J Phys Conf Ser 2021;2089:012058.
Goldberger AL, Amaral LA, Glass L, Hausdorff JM, Ivanov PC, MarkRG, et al. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation 2000;101:E215-20.
Baim DS, Colucci WS, Monrad ES, Smith HS, Wright RF, Lanoue A, et al. Survival of patients with severe congestive heart failure treated with oral milrinone. J Am Coll Cardiol 1986;7:661-70.
Gajendran MK, Khan MZ, Muazzam A, Khattak K. “2D Echo ClassificationUsing Deep Transfer Learning”. In: 2021 4th International Conference on Information and Computer Technologies (ICICT), IEEE; 2021. p. 1-5.
Shi H, Wang H, Qin C, Zhao L, Liu C. An incremental learning system for atrial fibrillation detection based on transfer learning and active learning. Comput Methods Programs Biomed 2020;187:105219.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
