Manosh Sur Choudhury
- Research Assistant, CCDS
Masters
+1 more affiliationDhaka University
Conference Paper2025
Deep Learning for Accurate Classification of Pulmonary Edema Severity in Chest X-Rays: A Comparative Evaluation of CNN Models with Grad-CAM Feature Visualization
Manosh Sur Choudhury, Rashedur Rahman, Siam Tahsin Bhuiyan, Sefatul Wasi, Saadia Binte Alam
2025 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)
In: 2025 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR)
IEEE, pp. 72-77
Abstract
Accurate classification of pulmonary edema severity is essential for timely diagnosis and effective management, as each severity level requires distinct therapeutic interventions. Differentiating between edema severity classes is particularly challenging due to the overlapping radiographic features of Chest X-ray (CXR). Deep learning, especially convolutional neural networks (CNNs), presents a promising solution by automating classification and identifying subtle features often difficult to detect through conventional methods. This study evaluates the performance of six traditional deep learning models for classifying pulmonary edema severity. Among these, CheXNet achieved the best performance, with an accuracy of 91% and an overall AUC score of 0.88. The findings highlight the importance of using pretraining on CXR datasets, which significantly enhances model performance compared to general pretraining. Additionally, Grad-CAM visualization was employed to interpret model decisions, identifying key radiographic features that contribute to accurate classification across different severity levels.
Keywords
Convolutional neural networkDeep learningFeature (linguistics)VisualizationPattern recognition (psychology)Pulmonary edemaRadiographyF1 score
CCDS Authors
Md. Rashedur Rahman, D. Eng.
- Co-Director, MIRA & NEST Wings
Assistant Professor
Department of Computer Science and Engineering
Independent University, Bangladesh
Siam Tahsin Bhuiyan
- Intern, CCDS
Bachelors
Independent University, Bangladesh
Saadia Binte Alam, PhD
- Director, MIRA & WiSE Wings
Associate Professor
Department of Computer Science and Engineering
Independent University, Bangladesh
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