Comparative Analysis of Deep Learning Architectures for Multi-Disease Classification of Single-Label Chest X-rays
Chest X-ray imaging remains the primary diagnostic tool for pulmonary and cardiac disorders worldwide, yet its accuracy is hampered by radiologist shortages and inter-observer variability. This study presents a systematic comparative evaluation of seven deep learning architectures for multi-class chest disease classification: ConvNeXt-Tiny, DenseNet121, DenseNet201, ResNet50, ViT-B/16, EfficientNetV2-M, and MobileNetV2. A balanced dataset of 18,080 chest X-ray images spanning five disease categories (Cardiomegaly, COVID-19, Normal, Pneumonia, and Tuberculosis) was constructed from three public repositories and partitioned at the patient level to prevent data leakage. All models were train...
| Authors | Ali M. Bahram, Saman Muhammad Omer, Hardi M. Mohammed |
|---|---|
| Primary category | eess.IV |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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