Empowering Healthcare: TinyML for Precise Lung Disease Classification

Youssef Abadade, Nabil Benamar, Miloud Bagaa () and Habiba Chaoui
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Youssef Abadade: System Engineering Laboratory, National School of Applied Siences, Ibn Tofail University of Kenitra, Kenitra B.P 242, Morocco
Nabil Benamar: School of Technology, Moulay Ismail University of Meknes, Meknes 50050, Morocco
Miloud Bagaa: Department of Electrical and Computer Engineering, University of Quebec at Trois-Rivieres, Trois-Rivieres, QC G8Z 4M3, Canada
Habiba Chaoui: System Engineering Laboratory, National School of Applied Siences, Ibn Tofail University of Kenitra, Kenitra B.P 242, Morocco

Future Internet, 2024, vol. 16, issue 11, 1-14

Abstract: Respiratory diseases such as asthma pose significant global health challenges, necessitating efficient and accessible diagnostic methods. The traditional stethoscope is widely used as a non-invasive and patient-friendly tool for diagnosing respiratory conditions through lung auscultation. However, it has limitations, such as a lack of recording functionality, dependence on the expertise and judgment of physicians, and the absence of noise-filtering capabilities. To overcome these limitations, digital stethoscopes have been developed to digitize and record lung sounds. Recently, there has been growing interest in the automated analysis of lung sounds using Deep Learning (DL). Nevertheless, the execution of large DL models in the cloud often leads to latency, dependency on internet connectivity, and potential privacy issues due to the transmission of sensitive health data. To address these challenges, we developed Tiny Machine Learning (TinyML) models for the real-time detection of respiratory conditions by using lung sound recordings, deployable on low-power, cost-effective devices like digital stethoscopes. We trained three machine learning models—a custom CNN, an Edge Impulse CNN, and a custom LSTM—on a publicly available lung sound dataset. Our data preprocessing included bandpass filtering and feature extraction through Mel-Frequency Cepstral Coefficients (MFCCs). We applied quantization techniques to ensure model efficiency. The custom CNN model achieved the highest performance, with 96% accuracy and 97% precision, recall, and F1-scores, while maintaining moderate resource usage. These findings highlight the potential of TinyML to provide accessible, reliable, and real-time diagnostic tools, particularly in remote and underserved areas, demonstrating the transformative impact of integrating advanced AI algorithms into portable medical devices. This advancement facilitates the prospect of automated respiratory health screening using lung sounds.

Keywords: TinyML; lung disease classification; early detection (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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