Design and Validation of a Mobile and IoT-Enabled Deep Learning Framework for Point-of-Care Pulmonary Disease Diagnosis

Abstract

Pulmonary diseases are a cause of illness and death worldwide. To improve outcomes early and accurate diagnosis is crucial. Typically diagnosing these diseases relies on hospital infrastructure, which can be hard to access in areas. Recent advancements in technology such as learning, Internet of Things (IoT) and mobile edge computing offer new possibilities for healthcare solutions that can be used anywhere. This paper presents a mobile and IoT-enabled deep learning system for diagnosing pulmonary diseases at the point of care. The system combines medical devices, mobile edge computing and simple deep learning models to quickly analyze pulmonary data [1]. A four-layer system is designed: data collection, edge processing, learning analysis, and cloud validation. Mathematical models are created to assess system performance, including latency, computational complexity, communication overhead and energy consumption. An experimental evaluation using available pulmonary imaging and respiratory sound datasets is also presented. The goal of this framework is to make pulmonary disease diagnosis more accessible, by providing clinical decision support. The results show that integrating learning with IoT healthcare systems can enable scalable and real-time pulmonary disease diagnosis. The proposed system aims to improve accessibility to disease diagnosis. Pulmonary disease diagnosis can be done quickly and efficiently using this system. Deep learning and IoT technologies make pulmonary disease diagnosis at the point of care. The system uses data to provide accurate diagnosis. Pulmonary diseases can be diagnosed using mobile and IoT-enabled deep learning framework.