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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
Institute of Advanced Engineering and Science (IAES).
2018
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| Subjects: | |
| 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 |
| Summary: | 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. |
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