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Feature Extraction and Classification of Respiratory Sound and Lung Diseases
Oral Presentation
Authors
1Tarbait Modares University (02182883373)
2Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran
Abstract
Bacteria, viruses, and fungi can cause respiratory
infections. It is usually possible to detect respiratory diseases early
by listening to the lung sounds with a stethoscope. In reality, lung
sound analysis is a time-consuming and difficult task that depends
on medical skills and recognition experience. Recent advances in
automatic respiratory sound recognition and classification have
attracted more attention. The outbreak of COVID-19 throughout
the world and the high patient numbers have placed a great deal
of pressure on medical professionals. A smart algorithm is
therefore a necessity to provide a faster and more accurate
detection of lung infections by automatically processing the sounds
of the lungs. This paper proposes two new lung sound feature
extraction, maximum entropy Gabor filter bank (MAGFB), and
maximum entropy Mel filter bank (MAMFB). The classification
is performed by a deep neural convolution network (DCNN). The
filter banks have been substituted, instead of the convolutional
layers. Experiments were conducted on the ICBHI 2017 Challenge
dataset (with eight classes). The proposed method has a better
performance compared to famous methods such as MFCC and
Wavelet transform. Particularly, the performance of the second
method is significant. For ICBHI 2017 challenge dataset, the
overall accuracy of MFCC, Wavelet, MAGFB and MAMFB were
76%, 85%, 81% and 91%, respectively.
infections. It is usually possible to detect respiratory diseases early
by listening to the lung sounds with a stethoscope. In reality, lung
sound analysis is a time-consuming and difficult task that depends
on medical skills and recognition experience. Recent advances in
automatic respiratory sound recognition and classification have
attracted more attention. The outbreak of COVID-19 throughout
the world and the high patient numbers have placed a great deal
of pressure on medical professionals. A smart algorithm is
therefore a necessity to provide a faster and more accurate
detection of lung infections by automatically processing the sounds
of the lungs. This paper proposes two new lung sound feature
extraction, maximum entropy Gabor filter bank (MAGFB), and
maximum entropy Mel filter bank (MAMFB). The classification
is performed by a deep neural convolution network (DCNN). The
filter banks have been substituted, instead of the convolutional
layers. Experiments were conducted on the ICBHI 2017 Challenge
dataset (with eight classes). The proposed method has a better
performance compared to famous methods such as MFCC and
Wavelet transform. Particularly, the performance of the second
method is significant. For ICBHI 2017 challenge dataset, the
overall accuracy of MFCC, Wavelet, MAGFB and MAMFB were
76%, 85%, 81% and 91%, respectively.
Keywords
Proceeding Title [Persian]
Feature Extraction and Classification of Respiratory Sound and Lung Diseases
Authors [Persian]
Abstract [Persian]
Bacteria, viruses, and fungi can cause respiratory
infections. It is usually possible to detect respiratory diseases early
by listening to the lung sounds with a stethoscope. In reality, lung
sound analysis is a time-consuming and difficult task that depends
on medical skills and recognition experience. Recent advances in
automatic respiratory sound recognition and classification have
attracted more attention. The outbreak of COVID-19 throughout
the world and the high patient numbers have placed a great deal
of pressure on medical professionals. A smart algorithm is
therefore a necessity to provide a faster and more accurate
detection of lung infections by automatically processing the sounds
of the lungs. This paper proposes two new lung sound feature
extraction, maximum entropy Gabor filter bank (MAGFB), and
maximum entropy Mel filter bank (MAMFB). The classification
is performed by a deep neural convolution network (DCNN). The
filter banks have been substituted, instead of the convolutional
layers. Experiments were conducted on the ICBHI 2017 Challenge
dataset (with eight classes). The proposed method has a better
performance compared to famous methods such as MFCC and
Wavelet transform. Particularly, the performance of the second
method is significant. For ICBHI 2017 challenge dataset, the
overall accuracy of MFCC, Wavelet, MAGFB and MAMFB were
76%, 85%, 81% and 91%, respectively.
infections. It is usually possible to detect respiratory diseases early
by listening to the lung sounds with a stethoscope. In reality, lung
sound analysis is a time-consuming and difficult task that depends
on medical skills and recognition experience. Recent advances in
automatic respiratory sound recognition and classification have
attracted more attention. The outbreak of COVID-19 throughout
the world and the high patient numbers have placed a great deal
of pressure on medical professionals. A smart algorithm is
therefore a necessity to provide a faster and more accurate
detection of lung infections by automatically processing the sounds
of the lungs. This paper proposes two new lung sound feature
extraction, maximum entropy Gabor filter bank (MAGFB), and
maximum entropy Mel filter bank (MAMFB). The classification
is performed by a deep neural convolution network (DCNN). The
filter banks have been substituted, instead of the convolutional
layers. Experiments were conducted on the ICBHI 2017 Challenge
dataset (with eight classes). The proposed method has a better
performance compared to famous methods such as MFCC and
Wavelet transform. Particularly, the performance of the second
method is significant. For ICBHI 2017 challenge dataset, the
overall accuracy of MFCC, Wavelet, MAGFB and MAMFB were
76%, 85%, 81% and 91%, respectively.
Keywords [Persian]
filter bank، feature extraction، classification، Deep Learning، respiratory sounds، lung diseases