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Scalable Unimodal and Multimodal Deep Learning for Multi-Label Chest Disease Detection: A Comparative Analysis.

Diğdem Orhan¹, Murat Ucan², Reda Alhajj^3,4,5

¹Department of Computer Engineering, Firat University, Elazig 23119, Turkey.

Diagnostics (Basel, Switzerland)

|March 14, 2026

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This summary is machine-generated.

Multimodal deep learning models integrating chest X-rays and clinical data significantly improve multi-label disease classification accuracy compared to image-only approaches. Larger datasets enhance model generalization and reduce performance variance, aiding diagnosis.

Area of Science:

Artificial Intelligence in Medicine
Medical Imaging Analysis
Computational Pathology

Background:

Accurate chest disease diagnosis is challenging, especially with coexisting pathologies.
Current deep learning models often use unimodal data, limiting clinical applicability.
This study addresses limitations by comparing unimodal and multimodal deep learning for chest disease classification.

Purpose of the Study:

To compare unimodal and multimodal deep learning models for multi-label chest disease classification.
To evaluate the impact of dataset scale on model performance and generalizability.
To assess the effectiveness of integrating chest X-ray images with clinical metadata.

Main Methods:

Developed twelve deep learning models using ResNet50, EfficientNetB3, and DenseNet121 architectures.

Keywords:

chest diseases deep learning multi-label classification multimodal fusion

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Configured models for unimodal (image-only) and multimodal (image + clinical data) learning.

Evaluated models on two NIH Chest X-ray Dataset scales (5,606 and 121,120 samples) using AUROC metrics.

Main Results:

Multimodal models consistently outperformed unimodal models across all architectures and dataset sizes.
Performance improvements were more significant with larger datasets.
Increased data volume enhanced model generalization and reduced performance variance, especially for rare diseases.

Conclusions:

Multimodal deep learning effectively enhances diagnostic accuracy for chest diseases.
Integrating clinical data with medical images improves multi-label classification.
Findings support developing robust clinical decision support systems for chest disease assessment.