Automated Cardiac Disease Prediction Using Composite GAN and DeepLab Model Article Swipe
Sohail Jabbar
,
Umar Raza
,
Muhammad Asif Habib
,
Muhammad Farhan
,
Saqib Saeed
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3589529
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1109/access.2025.3589529
Cardiovascular diseases remain the leading global cause of mortality, resulting in over 17 million deaths annually. Manual cardiac image interpretation is often subjective and varies significantly among clinicians. However, constraints like limited annotation and model generalization persist. We introduce GenDeep, a novel framework integrating an unsupervised Generative Adversarial Network (GAN) and DeepLab model for robust cardiac pathology classification from cine-MRI scans. The GAN component performs data augmentation to synthesize realistic pathological imagery, overcoming dataset constraints. Meanwhile, the DeepLab segmentation network exploits inter-slice spatial contexts for precise anatomical quantification. GenDeep is trained on over 4000 expert-annotated scans from the ACDC dataset, leveraging Apache Spark and Hadoop for efficient parallel data loading and preprocessing. The Generator maps noise vectors to synthetic MRIs while the Discriminator predicts disease labels and classifies images as real/fake. Weights are updated through backpropagation to refine image realism and classification accuracy. Once trained, the Generator produces additional pathological data to boost model generalization. The Discriminator then serves as the diagnostic classifier based on ventricular morphology from DeepLab segmentation. Extensive comparative testing on a held-out test set achieves 97% accuracy and 93% F1 Score, significantly exceeding benchmarks. Smooth convergence is verified with a low 2.21 MSE. These results highlight the effective integration of generative learning and segmentation for automated and reliable cardiac diagnosis.
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- Landing Page
- https://doi.org/10.1109/access.2025.3589529
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- 24
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All OpenAlex metadata
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https://openalex.org/W4413074730Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/access.2025.3589529Digital Object Identifier
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Automated Cardiac Disease Prediction Using Composite GAN and DeepLab ModelWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-01-01Full publication date if available
- Authors
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Sohail Jabbar, Umar Raza, Muhammad Asif Habib, Muhammad Farhan, Saqib SaeedList of authors in order
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https://doi.org/10.1109/access.2025.3589529Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1109/access.2025.3589529Direct OA link when available
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Composite number, Computer science, AlgorithmTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.accuracy. | 142 |
| abstract_inverted_index.annually. | 15 |
| abstract_inverted_index.automated | 209 |
| abstract_inverted_index.component | 63 |
| abstract_inverted_index.effective | 201 |
| abstract_inverted_index.efficient | 106 |
| abstract_inverted_index.exceeding | 186 |
| abstract_inverted_index.framework | 42 |
| abstract_inverted_index.highlight | 199 |
| abstract_inverted_index.introduce | 38 |
| abstract_inverted_index.pathology | 56 |
| abstract_inverted_index.realistic | 69 |
| abstract_inverted_index.resulting | 9 |
| abstract_inverted_index.synthetic | 118 |
| abstract_inverted_index.Generative | 46 |
| abstract_inverted_index.Meanwhile, | 75 |
| abstract_inverted_index.additional | 148 |
| abstract_inverted_index.anatomical | 86 |
| abstract_inverted_index.annotation | 32 |
| abstract_inverted_index.classifier | 162 |
| abstract_inverted_index.classifies | 127 |
| abstract_inverted_index.diagnosis. | 213 |
| abstract_inverted_index.diagnostic | 161 |
| abstract_inverted_index.generative | 204 |
| abstract_inverted_index.leveraging | 100 |
| abstract_inverted_index.morphology | 166 |
| abstract_inverted_index.mortality, | 8 |
| abstract_inverted_index.overcoming | 72 |
| abstract_inverted_index.real/fake. | 130 |
| abstract_inverted_index.subjective | 22 |
| abstract_inverted_index.synthesize | 68 |
| abstract_inverted_index.Adversarial | 47 |
| abstract_inverted_index.benchmarks. | 187 |
| abstract_inverted_index.clinicians. | 27 |
| abstract_inverted_index.comparative | 171 |
| abstract_inverted_index.constraints | 29 |
| abstract_inverted_index.convergence | 189 |
| abstract_inverted_index.integrating | 43 |
| abstract_inverted_index.integration | 202 |
| abstract_inverted_index.inter-slice | 81 |
| abstract_inverted_index.ventricular | 165 |
| abstract_inverted_index.augmentation | 66 |
| abstract_inverted_index.constraints. | 74 |
| abstract_inverted_index.pathological | 70, 149 |
| abstract_inverted_index.segmentation | 78, 207 |
| abstract_inverted_index.unsupervised | 45 |
| abstract_inverted_index.Discriminator | 122, 156 |
| abstract_inverted_index.segmentation. | 169 |
| abstract_inverted_index.significantly | 25, 185 |
| abstract_inverted_index.Cardiovascular | 0 |
| abstract_inverted_index.classification | 57, 141 |
| abstract_inverted_index.generalization | 35 |
| abstract_inverted_index.interpretation | 19 |
| abstract_inverted_index.preprocessing. | 111 |
| abstract_inverted_index.backpropagation | 135 |
| abstract_inverted_index.generalization. | 154 |
| abstract_inverted_index.quantification. | 87 |
| abstract_inverted_index.expert-annotated | 94 |
| cited_by_percentile_year | |
| countries_distinct_count | 3 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.43459778 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |