International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
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A Comprehensive Review on IoT-Enabled Intelligent Systems for COVID-19 Diagnosis Using Medical Imaging Jayesh N Patil1, Vikas M. Somvanshi2, Ashvini S. Kolate3 1Electrical Laboratory & Technical Assistant, Department of EE, SVKM IOT Dhule, Maharashtra, India 2Lecturer, Department of computer engineering SSVPS B S DEORE Polytechnic Dhule, Maharashtra, India
3Master of Computer Science , P.O. Nahata college Bhusawal , Maharashtra, India ---------------------------------------------------------------------***----------------------------------------------------------------------
Abstract - Automated diagnosis of COVID‑19 using
creating motivation for imaging-based diagnostic support systems. Medical imaging modalities such as chest X-ray (CXR) and computed tomography (CT) scans have therefore been leveraged for complementary diagnostics due to their wide availability and ability to reveal lung abnormalities associated with COVID-19.
medical imaging has emerged as a critical complement to traditional laboratory tests, enabling rapid and scalable screening amid global healthcare challenges. This review examines the integration of Internet of Things (IoT) technologies with artificial intelligence (AI) to build intelligent diagnostic systems that leverage chest X‑ray and CT imaging for COVID‑19 detection. We synthesize recent research on image preprocessing, feature extraction, optimization strategies, and classification models, highlighting how deep learning techniques including convolutional neural networks and transformer‑based architectures improve diagnostic accuracy and speed. Metaheuristic optimization and federated learning frameworks also play important roles in tuning model parameters and enabling privacy‑preserving collaborative training across institutions. Despite significant progress, key challenges remain, including dataset imbalance, limited generalizability across imaging devices and populations, and the lack of interpretable model explanations. Integration into clinical workflows is further complicated by computational complexity and data privacy concerns. Promising future directions include transformer‑based contextual learning, multimodal diagnostic models that combine imaging with clinical and IoT sensor data, federated and privacy‑preserving learning frameworks, and edge‑AI deployments tailored for real‑time, low‑latency environments. By consolidating current methods, limitations, and emerging trends, this review provides a roadmap for advancing robust, scalable, and clinically trustworthy AI‑assisted diagnostic systems that can support pandemic response and broader healthcare applications.
Manual interpretation of medical images is timeconsuming and subject to inter-observer variability, particularly under high workload conditions. Furthermore, subtle visual differences between COVID-19, other pneumonias, and normal lung states increase diagnostic difficulty. To address these limitations, intelligent systems that integrate Internet of Things (IoT) technologies with artificial intelligence (AI) have emerged, enabling automated, fast, and reliable classification and supporting real-time clinical decisions [1], [2]. This review analyzes advancements in IoT-enabled smart healthcare systems for COVID-19 diagnosis using imaging data. It synthesizes progress across preprocessing, feature extraction, optimization, classification, performance evaluation, challenges, and future directions.
2. IoT in Smart Healthcare for Pandemic Management IoT-based smart healthcare systems consist of interconnected medical sensors, imaging devices, and cloud analytics platforms. Digital X-ray and CT scanners provide primary sources of diagnostic data, while other IoT sensors capture physiological parameters such as heart rate and blood oxygen levels for comprehensive monitoring. These devices transmit data via secure communication protocols to centralized or edge computing platforms for storage and analysis [3]. Cloud and edge computing integration and quantum theory of IS [13]. It’s enhancing scalability, enabling real-time processing of high volumes of imaging data and AI analytics of "Potential of Quantum Computing IS network analysis [13]. Real-time monitoring architectures allow continuous data acquisition, automated alerts, and remote access for clinicians, which is crucial during large-scale outbreaks.
Key Words Transformer-based models, Vision transformers, Federated learning, Patient privacy, Multimodal diagnosis, IoT sensor data, Edge-AI deployment
1. INTRODUCTION The COVID-19 pandemic has posed severe challenges to global healthcare systems, emphasizing the critical need for rapid, accurate, and scalable diagnostic solutions. While reverse transcription polymerase chain reaction (RT-PCR) remains the clinical gold standard, it suffers from high cost, delayed results, and sensitivity limitations,
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