ArticleJournal of personalized medicine2023
An Efficient, Lightweight, Tiny 2D-CNN Ensemble Model to Detect Cardiomegaly in Heart CT Images.
Article in Journal of personalized medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- An algorithm for cardiac disease detection based on the magnetic resonance imaging.Scientific reports · 2025Article
- OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction.Frontiers in immunology · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
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Abstract
Cardiomegaly is a significant global health concern, especially in developing nations. Although advanced clinical care is available for newly diagnosed patients, many in resource-limited regions face late diagnoses and consequent increased mortality. This challenge is accentuated by a scarcity of radiography equipment and radiologists. Hence, we propose the development of a computer-aided diagnostic (CAD) system, specifically a lightweight, tiny 2D-CNN ensemble model, to facilitate early detection and, potentially, reduce mortality rates. Deep learning, with its subset of convolutional neural networks (CNN), has shown potential in visual applications, especially in medical image diagnosis. However, traditional deep CNNs often face compatibility issues with object-oriented human factor technology. Our proposed model aims to bridge this gap. Using CT scan images sourced from the Mendeley data center, our tiny 2D-CNN ensemble learning model achieved an accuracy of 96.32%, offering a promising tool for efficient and accurate cardiomegaly detection.
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Registered trials
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