ADVANCED MACHINE LEARNING FRAMEWORKS FOR PNEUMONIA DIAGNOSIS: INTEGRATING MULTIMODAL SIGNALS AND PRIVACY-PRESERVING TECHNIQUES

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Originally published in SYNAPSES: INSIGHTS ACROSS THE DISCIPLINES; Vol. 3 No. 5 (2026): SYNAPSES: INSIGHTS ACROSS THE DISCIPLINES; 126-132.
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Faizan, Abbas and Arshad and Wajiha, Batool and Saurabh, Kumar (2026) ADVANCED MACHINE LEARNING FRAMEWORKS FOR PNEUMONIA DIAGNOSIS: INTEGRATING MULTIMODAL SIGNALS AND PRIVACY-PRESERVING TECHNIQUES. SYNAPSES: INSIGHTS ACROSS THE DISCIPLINES; Vol. 3 No. 5 (2026): SYNAPSES: INSIGHTS ACROSS THE DISCIPLINES; 126-132.

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Abstract

Pneumonia remains a leading cause of morbidity and mortality worldwide, disproportionately affecting vulnerable populations such as young children and the elderly. Traditional diagnostic workflows, which heavily rely on manual triage and resource-intensive imaging assessments, are increasingly strained by rising patient volumes in emergency departments. 

Item Type: Article
Additional Information: Imported from Synapses: Insights Across the Disciplines
SWORD Depositor: Admin User
Depositing User: Admin User
Date Deposited: 08 Oct 2026 21:09
Last Modified: 08 Oct 2026 21:09
URI: https://universalpublishings.uz/id/eprint/20572

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