Advanced Diagnosis of Cardiac and Respiratory Diseases from Chest X-Ray Imagery Using Deep Learning Ensembles

Nakrani, Hemal and Shahra, Essa and Basurra, Shadi and Mohammad, Rasheed and Vakaj, Edlira and Jabbar, Waheb A. (2025) Advanced Diagnosis of Cardiac and Respiratory Diseases from Chest X-Ray Imagery Using Deep Learning Ensembles. Journal of Sensor and Actuator Networks, 14 (2). p. 44. ISSN 2224-2708

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Abstract

Chest X-ray interpretation is essential for diagnosing cardiac and respiratory diseases. This study introduces a deep learning ensemble approach that integrates Convolutional Neural Networks (CNNs), including ResNet-152, VGG19, EfficientNet, and a Vision Transformer (ViT), to enhance diagnostic accuracy. Using the NIH Chest X-ray dataset, the methodology involved comprehensive preprocessing, data augmentation, and model optimization techniques to address challenges such as label imbalance and feature variability. Among the individual models, VGG19 exhibited strong performance with a Hamming Loss of 0.1335 and high accuracy in detecting Edema, while ViT excelled in classifying certain conditions like Hernia. Despite the strengths of individual models, the ensemble meta-model achieved the best overall performance, with a Hamming Loss of 0.1408 and consistently higher ROC-AUC values across multiple diseases, demonstrating its superior capability to handle complex classification tasks. This robust ensemble learning framework underscores its potential for reliable and precise disease detection, offering significant improvements over traditional methods. The findings highlight the value of integrating diverse model architectures to address the complexities of multi-label chest X-ray classification, providing a pathway for more accurate, scalable, and accessible diagnostic tools in clinical practice.

Item Type:	Article
Identification Number:	10.3390/jsan14020044
Dates:	Date Event 16 April 2025 Accepted 18 April 2025 Published Online
Uncontrolled Keywords:	X-ray, VGG19, ViT, ResNet152, ensemble learning, deep learning
Subjects:	CAH10 - engineering and technology > CAH10-01 - engineering > CAH10-01-08 - electrical and electronic engineering
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Engineering
Depositing User:	Gemma Tonks
Date Deposited:	28 Apr 2025 14:59
Last Modified:	28 Apr 2025 14:59
URI:	https://www.open-access.bcu.ac.uk/id/eprint/16327

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