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Ba Mahel AS, Cao S, Zhang K, Chelloug SA, Alnashwan R, Muthanna MSA. Advanced integration of 2DCNN-GRU model for accurate identification of shockable life-threatening cardiac arrhythmias: a deep learning approach. Front Physiol 2024; 15:1429161. [PMID: 39072217 PMCID: PMC11272599 DOI: 10.3389/fphys.2024.1429161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 06/17/2024] [Indexed: 07/30/2024] Open
Abstract
Cardiovascular diseases remain one of the main threats to human health, significantly affecting the quality and life expectancy. Effective and prompt recognition of these diseases is crucial. This research aims to develop an effective novel hybrid method for automatically detecting dangerous arrhythmias based on cardiac patients' short electrocardiogram (ECG) fragments. This study suggests using a continuous wavelet transform (CWT) to convert ECG signals into images (scalograms) and examining the task of categorizing short 2-s segments of ECG signals into four groups of dangerous arrhythmias that are shockable, including ventricular flutter (C1), ventricular fibrillation (C2), ventricular tachycardia torsade de pointes (C3), and high-rate ventricular tachycardia (C4). We propose developing a novel hybrid neural network with a deep learning architecture to classify dangerous arrhythmias. This work utilizes actual electrocardiogram (ECG) data obtained from the PhysioNet database, alongside artificially generated ECG data produced by the Synthetic Minority Over-sampling Technique (SMOTE) approach, to address the issue of imbalanced class distribution for obtaining an accuracy-trained model. Experimental results demonstrate that the proposed approach achieves high accuracy, sensitivity, specificity, precision, and an F1-score of 97.75%, 97.75%, 99.25%, 97.75%, and 97.75%, respectively, in classifying all the four shockable classes of arrhythmias and are superior to traditional methods. Our work possesses significant clinical value in real-life scenarios since it has the potential to significantly enhance the diagnosis and treatment of life-threatening arrhythmias in individuals with cardiac disease. Furthermore, our model also has demonstrated adaptability and generality for two other datasets.
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Affiliation(s)
- Abduljabbar S. Ba Mahel
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shenghong Cao
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Kaixuan Zhang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Samia Allaoua Chelloug
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Rana Alnashwan
- Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia
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Rana S, Hosen MJ, Tonni TJ, Rony MAH, Fatema K, Hasan MZ, Rahman MT, Khan RT, Jan T, Whaiduzzaman M. DeepChestGNN: A Comprehensive Framework for Enhanced Lung Disease Identification through Advanced Graphical Deep Features. SENSORS (BASEL, SWITZERLAND) 2024; 24:2830. [PMID: 38732936 PMCID: PMC11086108 DOI: 10.3390/s24092830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/02/2024] [Revised: 04/06/2024] [Accepted: 04/16/2024] [Indexed: 05/13/2024]
Abstract
Lung diseases are the third-leading cause of mortality in the world. Due to compromised lung function, respiratory difficulties, and physiological complications, lung disease brought on by toxic substances, pollution, infections, or smoking results in millions of deaths every year. Chest X-ray images pose a challenge for classification due to their visual similarity, leading to confusion among radiologists. To imitate those issues, we created an automated system with a large data hub that contains 17 datasets of chest X-ray images for a total of 71,096, and we aim to classify ten different disease classes. For combining various resources, our large datasets contain noise and annotations, class imbalances, data redundancy, etc. We conducted several image pre-processing techniques to eliminate noise and artifacts from images, such as resizing, de-annotation, CLAHE, and filtering. The elastic deformation augmentation technique also generates a balanced dataset. Then, we developed DeepChestGNN, a novel medical image classification model utilizing a deep convolutional neural network (DCNN) to extract 100 significant deep features indicative of various lung diseases. This model, incorporating Batch Normalization, MaxPooling, and Dropout layers, achieved a remarkable 99.74% accuracy in extensive trials. By combining graph neural networks (GNNs) with feedforward layers, the architecture is very flexible when it comes to working with graph data for accurate lung disease classification. This study highlights the significant impact of combining advanced research with clinical application potential in diagnosing lung diseases, providing an optimal framework for precise and efficient disease identification and classification.
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Affiliation(s)
- Shakil Rana
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh; (S.R.); (M.J.H.); (T.J.T.); (M.A.H.R.); (K.F.); (M.Z.H.)
| | - Md Jabed Hosen
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh; (S.R.); (M.J.H.); (T.J.T.); (M.A.H.R.); (K.F.); (M.Z.H.)
| | - Tasnim Jahan Tonni
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh; (S.R.); (M.J.H.); (T.J.T.); (M.A.H.R.); (K.F.); (M.Z.H.)
| | - Md. Awlad Hossen Rony
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh; (S.R.); (M.J.H.); (T.J.T.); (M.A.H.R.); (K.F.); (M.Z.H.)
| | - Kaniz Fatema
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh; (S.R.); (M.J.H.); (T.J.T.); (M.A.H.R.); (K.F.); (M.Z.H.)
| | - Md. Zahid Hasan
- Health Informatics Research Laboratory (HIRL), Department of Computer Science and Engineering, Daffodil International University, Dhaka 1207, Bangladesh; (S.R.); (M.J.H.); (T.J.T.); (M.A.H.R.); (K.F.); (M.Z.H.)
| | - Md. Tanvir Rahman
- School of Health and Rehabilitation Sciences, The University of Queensland, St. Lucia, QLD 4072, Australia
- Department of Information and Communication Technology, Mawlana Bhashani Science and Technology University, Tangail 1902, Bangladesh
| | - Risala Tasin Khan
- Institute of Information Technology, Jahangirnagar University, Dhaka 1342, Bangladesh;
| | - Tony Jan
- Centre for Artificial Intelligence Research and Optimisation (AIRO), Torrens University, Ultimo, NSW 2007, Australia;
| | - Md Whaiduzzaman
- Centre for Artificial Intelligence Research and Optimisation (AIRO), Torrens University, Ultimo, NSW 2007, Australia;
- School of Information Systems, Queensland University of Technology, Brisbane, QLD 4000, Australia
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Saju B, Tressa N, Dhanaraj RK, Tharewal S, Mathew JC, Pelusi D. Effective multi-class lungdisease classification using the hybridfeature engineering mechanism. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2023; 20:20245-20273. [PMID: 38052644 DOI: 10.3934/mbe.2023896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/07/2023]
Abstract
The utilization of computational models in the field of medical image classification is an ongoing and unstoppable trend, driven by the pursuit of aiding medical professionals in achieving swift and precise diagnoses. Post COVID-19, many researchers are studying better classification and diagnosis of lung diseases particularly, as it was reported that one of the very few diseases greatly affecting human beings was related to lungs. This research study, as presented in the paper, introduces an advanced computer-assisted model that is specifically tailored for the classification of 13 lung diseases using deep learning techniques, with a focus on analyzing chest radiograph images. The work flows from data collection, image quality enhancement, feature extraction to a comparative classification performance analysis. For data collection, an open-source data set consisting of 112,000 chest X-Ray images was used. Since, the quality of the pictures was significant for the work, enhanced image quality is achieved through preprocessing techniques such as Otsu-based binary conversion, contrast limited adaptive histogram equalization-driven noise reduction, and Canny edge detection. Feature extraction incorporates connected regions, histogram of oriented gradients, gray-level co-occurrence matrix and Haar wavelet transformation, complemented by feature selection via regularized neighbourhood component analysis. The paper proposes an optimized hybrid model, improved Aquila optimization convolutional neural networks (CNN), which is a combination of optimized CNN and DENSENET121 with applied batch equalization, which provides novelty for the model compared with other similar works. The comparative evaluation of classification performance among CNN, DENSENET121 and the proposed hybrid model is also done to find the results. The findings highlight the proposed hybrid model's supremacy, boasting 97.00% accuracy, 94.00% precision, 96.00% sensitivity, 96.00% specificity and 95.00% F1-score. In the future, potential avenues encompass exploring explainable machine learning for discerning model decisions and optimizing performance through strategic model restructuring.
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Affiliation(s)
- Binju Saju
- Department of Master of Computer Applications, New Horizon College of Engineering, Bengaluru, India
| | - Neethu Tressa
- Department of Master of Computer Applications, New Horizon College of Engineering, Bengaluru, India
| | - Rajesh Kumar Dhanaraj
- Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International University, Pune, India
| | - Sumegh Tharewal
- Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International University, Pune, India
| | | | - Danilo Pelusi
- Department of Communication Sciences, University of Teramo, Teramo, Italy
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