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Classification of normal and crackles respiratory sounds into healthy and lung cancer groups

Abdul Malik, Noreha and Idris, W. and Gunawan, Teddy Surya and Olanrewaju, Rashidah Funke and Ibrahim, Siti Noorjannah (2018) Classification of normal and crackles respiratory sounds into healthy and lung cancer groups. International Journal of Electrical and Computer Engineering (IJECE), 8 (3). pp. 11530-1538. ISSN 2088-8708

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Lung cancer is the most common cancer worldwide and the third most common cancer in Malaysia. Due to its high prevalence worldwide and in Malaysia, it is an utmost importance to have the disease detected at an early stage which would result in a higher chance of cure and possibly better survival. The current methods used for lung cancer screening might not be simple, inexpensive and safe and not readily accessible in outpatient clinics. In this paper, we present the classification of normal and crackles sounds acquired from 20 healthy and 23 lung cancer patients, respectively using Artificial Neural Network. Firstly, the sounds signals were decomposed into seven different frequency bands using Discrete Wavelet Transform (DWT) based on two different mother wavelets namely Daubechies 7 (db7) and Haar. Secondly, mean, standard deviation and maximum PSD of the detail coefficients for five frequency bands (D3, D4, D5, D6, and D7) were calculated as features. Fifteen features were used as input to the ANN classifier. The results of classification show that db7 based performed better than Haar with perfect 100% sensitivity, specificity and accuracy for testing and validation stages when using 15 nodes at the hidden layer. While for Haar, only testing stage shows the perfect 100% for sensitivity, specificity, and accuracy when using 10 nodes at the hidden layer.

Item Type: Article (Journal)
Additional Information: 3920/66270
Uncontrolled Keywords: ANN; Crackles; DWT; Lung cancer; Respiratory sound Lung cancer Respiratory sound
Subjects: R Medicine > R Medicine (General)
T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Dr. Rashidah Funke Olanrewaju
Date Deposited: 02 Oct 2018 17:01
Last Modified: 28 Jun 2019 15:47
URI: http://irep.iium.edu.my/id/eprint/66270

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