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

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...

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Main Authors:	Abdul Malik, Noreha, Idris, W., Gunawan, Teddy Surya, Olanrewaju, Rashidah Funke, Ibrahim, Siti Noorjannah
Format:	Article
Language:	English English
Published:	Institute of Advanced Engineering and Science (IAES). 2018
Subjects:	R Medicine (General) T Technology (General)
Online Access:	http://irep.iium.edu.my/66270/ http://irep.iium.edu.my/66270/ http://irep.iium.edu.my/66270/ http://irep.iium.edu.my/66270/1/Classification%20of%20Normal%20and%20Crackles%20Respiratory%20Sounds%20into%20Healthy%20and%20Lung%20Cancer%20Groups.pdf http://irep.iium.edu.my/66270/7/66270_Classification%20of%20normal%20and%20crackles_scopus.pdf

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spelling	iium-662702019-06-28T07:47:25Z http://irep.iium.edu.my/66270/ Classification of normal and crackles respiratory sounds into healthy and lung cancer groups Abdul Malik, Noreha Idris, W. Gunawan, Teddy Surya Olanrewaju, Rashidah Funke Ibrahim, Siti Noorjannah R Medicine (General) T Technology (General) 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. Institute of Advanced Engineering and Science (IAES). 2018-06 Article PeerReviewed application/pdf en http://irep.iium.edu.my/66270/1/Classification%20of%20Normal%20and%20Crackles%20Respiratory%20Sounds%20into%20Healthy%20and%20Lung%20Cancer%20Groups.pdf application/pdf en http://irep.iium.edu.my/66270/7/66270_Classification%20of%20normal%20and%20crackles_scopus.pdf 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 http://www.iaescore.com/journals/index.php/IJECE/article/view/11761/8695 10.11591/ijece.v8i3.pp1530-1538
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English
topic	R Medicine (General) T Technology (General)
spellingShingle	R Medicine (General) T Technology (General) Abdul Malik, Noreha Idris, W. Gunawan, Teddy Surya Olanrewaju, Rashidah Funke Ibrahim, Siti Noorjannah Classification of normal and crackles respiratory sounds into healthy and lung cancer groups
description	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.
format	Article
author	Abdul Malik, Noreha Idris, W. Gunawan, Teddy Surya Olanrewaju, Rashidah Funke Ibrahim, Siti Noorjannah
author_facet	Abdul Malik, Noreha Idris, W. Gunawan, Teddy Surya Olanrewaju, Rashidah Funke Ibrahim, Siti Noorjannah
author_sort	Abdul Malik, Noreha
title	Classification of normal and crackles respiratory sounds into healthy and lung cancer groups
title_short	Classification of normal and crackles respiratory sounds into healthy and lung cancer groups
title_full	Classification of normal and crackles respiratory sounds into healthy and lung cancer groups
title_fullStr	Classification of normal and crackles respiratory sounds into healthy and lung cancer groups
title_full_unstemmed	Classification of normal and crackles respiratory sounds into healthy and lung cancer groups
title_sort	classification of normal and crackles respiratory sounds into healthy and lung cancer groups
publisher	Institute of Advanced Engineering and Science (IAES).
publishDate	2018
url	http://irep.iium.edu.my/66270/ http://irep.iium.edu.my/66270/ http://irep.iium.edu.my/66270/ http://irep.iium.edu.my/66270/1/Classification%20of%20Normal%20and%20Crackles%20Respiratory%20Sounds%20into%20Healthy%20and%20Lung%20Cancer%20Groups.pdf http://irep.iium.edu.my/66270/7/66270_Classification%20of%20normal%20and%20crackles_scopus.pdf
first_indexed	2023-09-18T21:34:04Z
last_indexed	2023-09-18T21:34:04Z
_version_	1777412702837145600

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

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