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A Lightweight CNN Model for Detecting Respiratory Diseases from Lung Auscultation Sounds using EMD-CWT-based Hybrid Scalogram

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Document pages: 9 pages

Abstract: Listening to lung sounds through auscultation is vital in examining therespiratory system for abnormalities. Automated analysis of lung auscultationsounds can be beneficial to the health systems in low-resource settings wherethere is a lack of skilled physicians. In this work, we propose a lightweightconvolutional neural network (CNN) architecture to classify respiratorydiseases using hybrid scalogram-based features of lung sounds. The hybridscalogram features utilize the empirical mode decomposition (EMD) andcontinuous wavelet transform (CWT). The proposed scheme s performance isstudied using a patient independent train-validation set from the publiclyavailable ICBHI 2017 lung sound dataset. Employing the proposed framework,weighted accuracy scores of 99.20 for ternary chronic classification and99.05 for six-class pathological classification are achieved, which outperformwell-known and much larger VGG16 in terms of accuracy by 0.52 and 1.77 respectively. The proposed CNN model also outperforms other contemporarylightweight models while being computationally comparable.

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