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All Journal Journal of Computing Theories and Applications IJISCS (International Journal of Information System and Computer Science)
Oluwagbemi, Johnson Bisi
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Multimodal Deep Learning for Pneumonia Detection Using Wearable Sensors: Toward an Edge-Cloud Framework Ibam, Emmanuel Onwako; Oluwagbemi, Johnson Bisi
Journal of Computing Theories and Applications Vol. 3 No. 3 (2026): JCTA 3(3) 2026
Publisher : Universitas Dian Nuswantoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/jcta.14944

Abstract

Pneumonia remains a leading cause of morbidity and mortality worldwide, particularly in resource-limited settings and among elderly populations, where timely diagnosis and continuous monitoring are often constrained by limited clinical infrastructure. This study presents an edge–cloud–integrated framework for early pneumonia risk monitoring, leveraging multimodal wearable sensors and deep learning to support continuous short-duration monitoring. The proposed system is designed to operate in near real time under simulated deployment conditions, continuously acquiring and analyzing physiological signals (respiratory rate, heart rate, SpO₂, and body temperature) alongside event-driven acoustic biomarkers (cough sounds) within a distributed architecture. A lightweight edge module performs local signal preprocessing and anomaly triage, selectively transmitting salient information to a cloud-based multimodal deep learning model for refined risk estimation and interpretability analysis. The framework was evaluated using a multi-source dataset comprising public repositories (MIMIC-III and Coswara) and a clinically supervised wearable study conducted in two Nigerian hospitals, resulting in 718  hours of quality-controlled multimodal monitoring data. In a pooled multi-source evaluation, the system achieved an AUC of 0.95, while in a clinically realistic local-only evaluation, the AUC was 0.86, reflecting a consistent but preliminary diagnostic signal. These results highlight the importance of local data adaptation for real-world applicability and suggest that multimodal AI can provide meaningful early risk indicators under resource constraints. Beyond predictive performance, this work demonstrates the feasibility of integrating multimodal learning, edge–cloud computation, and explainable analytics into a deployment-aware, privacy-preserving monitoring framework for low-resource healthcare environments.
DIAGNOSTIC ACCURACY OF DEEP NEURAL NETWORKS FOR PNEUMONIA AND COVID-19 DETECTION ON MEDICAL IMAGING: A SYSTEMATIC REVIEW AND META-ANALYSIS Oluwagbemi, Johnson Bisi; Akinbo, Racheal Shade; Mesioye, Ayobami Emmanuel
IJISCS (International Journal of Information System and Computer Science) Vol 9, No 3 (2025): IJISCS (International Journal of Information System and Computer Science)
Publisher : Bakti Nusantara Institute

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56327/ijiscs.v9i3.1857

Abstract

Pneumonia and COVID-19 remain leading causes of universal morbidity and mortality, with timely and precise diagnosis essential for effective patient management. This systematic review and meta-analysis assessed the diagnostic accuracy of deep neural networks in detecting pneumonia and COVID-19 across main medical imaging modalities. Comprehensive searches of PubMed, Scopus, Web of Science, IEEE Xplore and Cochrane Library identified 80 eligible studies published between 2017 and 2025. Included studies used chest X-ray (CXR), computed tomography (CT) and lung ultrasound (LUS) images analyzed through convolutional neural networks, transformer-based and hybrid deep models. Pooled diagnostic performance was synthesized using a bivariate random-effects model and hierarchical summary receiver operating characteristic analysis. Overall pooled sensitivity and specificity were 0.88 (95% CI: 0.84-0.91) and 0.90 (95% CI: 0.86-0.92), respectively, with an area under the curve of 0.93, indicating high discriminative capability. Subgroup analyses revealed CT-based models outperformed CXR and LUS, while transformer architectures marginally exceeded CNNs. In addition, external validation studies steadily reported lower accuracy than internal validations, reflecting limited model generalizability. Risk of bias assessment using QUADAS-2 emphasized concerns related to patient selection, data leakage and non-standardized reference criteria. Despite moderate heterogeneity (I² = 39-52%) and potential publication bias, findings confirm the substantial potential of DNNs as decision-support tools for fast, scalable and reliable respiratory disease diagnosis. However, broader clinical adoption demands multicenter validation, transparency and adherence to ethical AI standards. This study provides evidence-based insights into the current performance and translational readiness of AI-driven diagnostic imaging for pneumonia and COVID-19.
Co-Authors Akinbo, Racheal Shade Ibam, Emmanuel Onwako Mesioye, Ayobami Emmanuel