Lung Disease Classification using Deep Convolutional Neural Network

Abstract

The advanced technologies are essential to achieving the improvement of medicine. More specifically, an extensive investigation in a partnership among researchers, health care providers, and patients is integral to bringing precise and customized treatment strategies in taking care of various diseases. This paper aims to assess the degree of accuracy acceptable in the medical field by utilizing deep learning to publicly available data. First, we extracted spectrogram features and labels of the annotated lung sound samples and used them as an input to our 2D Convolutional Neural Network (CNN) model. Secondly, we normalized the lung sounds to remove the peak values and noise from them. For deep learning classification, publicly available data was not sufficient to conduct the learning process. Finally, we have created a deep learning model called Lung Disease Classification (LDC), combined with advanced data normaliza-tion and data augmentation techniques, for high-performance classification in lung disease diagnosis. The final accuracy obtained after the normalization and augmentation was approximately 97%. The proposed model paves the way for adequate assessment of the degree of accuracy acceptable in the medical field and guarantees better performance than other previously reported approaches.

Full text

Lung Disease Classification using Deep Convolutional
Neural Network
Zeenat Tariq, Sayed Khushal Shah, Yugyung Lee
School of Computing and Engineering, University of Missouri-Kansas City, USA
zt2gc@mail.umkc.edu, ssqn7@mail.umkc.edu, leeyu@umkc.edu
Abstract—The advanced technologies are essential to achieving
the improvement of medicine. More specifically, an extensive
investigation in a partnership among researchers, health care
providers, and patients is integral to bringing precise and
customized treatment strategies in taking care of various diseases.
This paper aims to assess the degree of accuracy acceptable in
the medical field by utilizing deep learning to publicly available
data. First, we extracted spectrogram features and labels of the
annotated lung sound samples and used them as an input to our
2D Convolutional Neural Network (CNN) model. Secondly, we
normalized the lung sounds to remove the peak values and noise
from them. For deep learning classification, publicly available
data was not sufficient to conduct the learning process. Finally,
we have created a deep learning model called Lung Disease
Classification (LDC), combined with advanced data normaliza-
tion and data augmentation techniques, for high-performance
classification in lung disease diagnosis.
The final accuracy obtained after the normalization and
augmentation was approximately 97%. The proposed model
paves the way for adequate assessment of the degree of accuracy
acceptable in the medical field and guarantees better performance
than other previously reported approaches.
Index Terms—Data normalization, Data augmentation, Con-
volutional neural network, Lungs sound classification, Deep
learning.
I. INTRODUCTION
Lung sounds are the acoustic signals generated from breath-
ing. An auscultatory method has been applied widely by
physicians to examine lung sounds associated with different
respiratory symptoms. The auscultatory method has been the
easiest way to diagnose patients with respiratory diseases such
as pneumonia, asthma, and bronchiectasis [1], [2]. However,
it is a manual process, which takes a lot of time and creates
a possibility of more or less accuracy due to the complexity
of the sound patterns and characteristics. This may involve
a high risk of missed data, leading to underdiagnosed or
misdiagnosed results [3], [4]. The accuracy of auscultation is
not always correct and reliable since it was found that in one
of the studies, the residents were not able to identify 100% of
wheezing sounds in a series of pulmonary disease sounds [5].
Machine learning plays an important role in classifying
different types of sounds through multiple algorithms [6].
Deep learning is a branch of machine learning, which has
attracted a lot of attention due to its high performance in
prediction and classification. These learning techniques are
among the fastest-growing fields nowadays in the area of audio
classification [7]. These classifiers outperform humans due to
the ability to ignore noise and memory issues.
In this paper, we have applied deep learning techniques
for better classification of our results on the diagnosis of
respiratory symptoms. We propose our model that is uniquely
designed with a popular deep learning network, Convolutional
Neural Network (CNN). Specifically, we introduce various
advanced preprocessing techniques such as normalization and
augmentation for an effective lung sounds classification. The
classification is based on the spectrogram features that are
extracted from the audio dataset. The traditional classification
results vary due to the existence of noise in the audio samples,
which are due to the environmental interference. The existing
CNN approaches have adopted a different architecture and
therefore obtaining an accuracy between 80 - 95% with very
high consumption of memory, which are purely based on audio
feature techniques. The dataset used for the experimentation
is a public dataset provided for research in [8].
One of the challenges in the research was finding the data
that is publicly available and cleaning the data that are not
recorded properly and cannot be accepted if it is given as
an input to a class. Because of directly recording audio from
lungs, the audio samples may have some noise coming from
the heart or any other sounds that exist in the body. To improve
the accuracy, we have applied the data normalization technique
on the original data to rescale the audio samples in a better
position and average values for better accuracy.
Deep learning relies on large amounts of data. Due to
limited amount of publicly available data, there is limited
research progress in this field. To tackle this problem. we
have proposed a solution that is known in deep learning field
as Data Augmentation [9]. Therefore, to improve our results
further, we needed larger amounts of data. For that purpose,
we have applied our data augmentation techniques, which can
help the CNN model report a better accuracy. Finally, our
model was observed to outperform all other models that are
already researched so far. Large amounts of data could not be
experimented by other researchers while data augmentation
made us stand out and outperform all other researches.
II. RE LATE D WORK
Chen et al. [10] proposed a novel solution for lung sounds
classification by using a publicly available dataset. The dataset
was divided into three categories, i.e., wheezes, crackles and
normal. They proposed a detection method using optimized
S-transformed (OST) and deep residual networks (ResNets).
They performed preprocessing on the audio samples by us-

Fig. 1. Block Diagram for LDC System
ing OST, which rescaled the features for ResNets. Dalal et
al. [11] has compared four methods of machine learning
approaches for the purpose of lung sound classification us-
ing lungs dataset. CNN according to their experimentation
outperformed all other classifiers. However, this all depend
on the batch size and number of epochs. Although they
have obtained an accuracy of approximately 97% but their
machine utilization was very high by applying almost 1 million
or more epochs. Rupesh et al. [12] have reviewed several
features extraction and classification techniques for pulmonary
obstructive diseases such as COPD and asthma. In their review,
the feature extraction used were FFT, STFT, spectrograms
and wavelet transform. The best accuracy that was reported
for CNN was approximately 95% after all possible efforts.
Chen et al. [13] proposed a solution for automatic early
detection of a disease using CNN for heart and lungs. They
collected data from volunteer patients, which were manually
annotated by doctors for the consideration of experiments. The
dataset was too limited to have any consequences for results.
Salamon and Bello [14] presented the data augmentation
technique for environmental sound classification using CNN.
The deformation of audio was performed through stretching,
pitch shifting, dynamic range compression, and background
noise. Piczak [15] proposed a CNN model for classification
of environmental sounds. Their 1D CNN architecture consists
of two convolutional rectified layers by applying max pooling,
two fully connected hidden layers, and a softmax output layer.
The data was augmented through random time delays and
pitch shifting. Mel spectrograms were extracted from all audio
samples, resampled and normalized.
III. MET HO D
A. Data Normalization
In this paper, we have evaluated existing normalization
techniques and selected three best ones for the evaluation.
Root Mean Square Normalization In the Root Mean Square
(RMS) Normalization, the amplitude level takes the average
of a signal amplitude where it does not work as the arithmetic
mean of a signal received.
The RMS level is useful for finding the signal strength based
on the amplitude regardless of positive or negative values of
the signal. For a given signal, x=x1, x2, . . . , xn, the RMS
value, xrms is:
xrms =rx2
n=r1
n(x2
1+x2
2+. . . +x2
n)(1)
The signal amplitude normalization can only be possible if
we can figure out the scaling factor that can perform the linear
gain change. There is a possibility to scale a signal with an
amplitude that is higher than 1 or less than zero 0 decibels
(db). For applying the linear gain change we can rearrange
the above RMS level formula as shown in Equation 2 where
R has a linear scale.
R=r1
n[(ax1)2+ (ax2)2+. . . + (axn)2]
a=snR2
(x1)2+ (x2)2+. . . + (xn)2
(2)
Peak Normalization In peak normalization, the peak signal
level is analyzed in decibels relative to full scale (dBFS) and
for the purpose of normalization, it amplifies the volume of the
signal in such a manner that the output gets 0 dB maximum.
This process can scale the amplitude of all input audio signals
in such a way that the highest amplitude of the signal has a
value of 1. The output signal based on above scaling can be
mathematically calculated as
out =1
max(abs(in)).in (3)
EBU Union Standard R128 Normalization European Broad-
casting Union (EBU) Standard R128 Normalization focused
on measuring the average loudness of a program in the
normalization of audio signals.
B. Data Augmentation
We have experimented different types of data augmentation
and concluded to experiment our results in three different ways
such as time stretching, pitch shifting, and dynamic range
compression [16]. Initially, the original data consists of 920
audio samples. After applying the augmentation techniques,
the total audio samples obtained including the original audio
samples were 11960. The files size that occupied the storage

Fig. 2. Spectrogram Feature Extraction
was 26GB. For data augmentation, it is important to select the
deformation patterns in such a way that the original labels are
maintained and augmented.
Time Stretching For augmentation, the speed of the audio
sample is changed and is increased or decreased by some
factors [17]. We used four audio speeds, i.e., 0.5, 0.7, 1.2
and 1.5, along with the original audio sample files.
Pitch Shifting For data augmentation, the pitch of the audio
samples are either decreased or increased by 4 values (semi-
tones) [18]. The duration of the audio samples is kept constant
similar to the original audio samples i.e.,4 - 10 seconds. The
value changed in semitones ranged between -2, -1, 1, 2.
Dynamic Range Compression This technique compresses the
dynamic range of the audio sample by four parameters. Among
them, three are taken from Dolby E Standard and 1 is taken
from ice cast radio live streaming server.
C. Network Model
Figure 1 shows the block diagram for the LDC system.
Data Normalization and data augmentation techniques are
applied to Lungs sound data where spectrogram features are
extracted from the regenerated audio samples. These extracted
features are passed to 2D CNN for classification. There are
two main components of a convolutional neural network,
i.e.,feature extractor and classifier. The feature extractor extract
the spectrogram features from the audio signal and pass them
to a classifier to classify the signals into their appropriate
categories. The classifier consists of different convolutional
and pooling layers, followed by linear activation and fully
connected layers, which are used for classification purpose.
The mathematical form of the convolutional layers can be
found in Equation 4 and 5.
[xl
i,j,k =X
aX
bX
c
w(l−1,f)
i,j,k y(l−1)
i+a,j+b,k+c+biasf](4)
[yl
i,j,k =σ(x(l)
i,j,k)] (5)
The output layer is represented by yl
i,j,k where as the 3-
dimensional input tensor is denoted by i, j, k. The weights
for filters are denoted by w(l)
i,j,k and σ(x(l)
i,j,k)describes the
sigmoid function for linear activation. The fully connected is
layer is represented by Equation 6 and 7.
[x(l)
iX
j
wl−1
i,j yl−1
j+biasl−1
j](6)
Fig. 3. Classification Accuracy: (a) Original, (b) Normalized, (c) Augmented
[yl
i,j,k =σ(xl
i,j,k)] (7)
The 2D CNN architecture is composed of 5 layers. The first
three are the convolutional layers, which are enclosed by max
pool layer and finally they are followed two fully connected
layers. We extracted librosa features for Mel spectrograms
because for noise data spectrograms are considered as the best
to differentiate between type of sounds. During the extraction
of features, we have used window size and hope the size of 23
ms. As the sound clips vary between 3 to 10 seconds so that
we kept the extraction to 3 seconds to make every bit of the
sound clip usable. The input from the sound clips is reshaped
and X∈R128x128 shape is provided to the classifier.
The first layer takes the reshaped features as an input in
the form of spectrograms with 24 filters. It takes the shape
of [24x1x5x5]. The stride in this layer is [4x2] with ReLU
as the activation function. The second layer has 48 filters of
the shape [48x24x5x5] with [4x2] stride max-pooling layer
and using ReLU as the activation layer. The third layer also
takes 48 filters with receptive field [5x5] resulting in shape
[48x48x5x5], and the activation is ReLU without pooling.
Finally, the fourth layer has 64 hidden units resulting in shape
[2000x64] with ReLU activation and [64x10] with softmax
activation. In the top layer, we considered [5x5], which is a
very small receptive layer due to the localized patterns.
IV. EXPERIMENTAL DESI GN A ND RESULTS
The dataset is composed of a total 5.5 hours of recording,
which are further divided into recording samples of 126
patients. The categories include Asthma, Chronic Obstructive
Pulmonary Disease (COPD), Healthy, Upper Respiratory Tract
Infection (URTI), Lower Respiratory Tract Infection (LRTI),
and Pneumonia. . Table 1 shows the categories and the number
of data in the dataset.
We used librosa [19] for the spectrogram feature extraction.
Figure 2 shows the features extracted from the Lung Sounds
dataset for spectrograms.

TABLE I
ORIGINAL AND AUGMENTED DATA SIZE
ID Name of Disease #Audio File #Augmented Audio File
1 Asthma 1 13
2 Bronchiectasis 29 377
3 COPD 785 10205
4 Healthy 35 455
5 LRTI 2 26
6 Pneumonia 37 481
7 URTI 31 403
TABLE II
LDC SYS TE M MODE L RESU LT COMPARISON
Model Technique Accuracy
Model 1 Original Data 83%
Model 2 Peak Value Normalization 86%
Model 3 RMS Normalization 87%
Model 4 EBU Normalization 88%
Model 5 Augmentation applied on Original Data 93%
Model 6 Normalized Peak Value Augmentation 92%
Model 7 Normalized RMS Value Augmentation 94%
Model 8 Normalized EBU Value Augmentation 97%
We have designed our experiments to evaluate the proposed
lung sound classification based on 2D CNN with the lungs
sound dataset. The dataset is split into 70% and 30% for
training/testing. The batch size was 32 and the number of
epochs was fixed at 100 to avoid any over/under-fitting. The
results of each instance for the LDC system is shown in Table
II. It was observed during our experimentation stage that the
highest accuracy achieved by the existing research is 97%,
which is dependent on GPU usage and memory consumption.
It can be seen from Table II that the LDC experimentation
for Models 1-8. Although the data was not enough for the
training, we were able to achieve good results.
Our model is experimented for 2D CNN classification
network on the original dataset, which reported an accuracy
of approximately 83%. Further, we have applied the three
types of normalization i.e., Peak, RMS and EBU, and obtained
an accuracy of 86%, 87% and 88%, respectively. The data
augmentation is considered as the trend making technique in
deep learning for small datasets. Further, the accuracy reported
from the 2D CNN for the original data augmentation was
93%. We have also applied three augmentation techniques on
normalized data and the highest accuracy achieved was 97%.
Even though the data was not enough and had a lot of variation
and environmental interference in recording (i.e.,heart beat,
running fan), it was observed that our technique has achieved
a very good accuracy in comparison with the state of the art
research considering feature based approach.
Figure 3(a)-(c) represents the accuracy of 2D CNN network
based on lungs dataset for original, normalized and augmented
data. It was analyzed that when the data was in original form,
CNN ran into overfitting and the highest accuracy reported was
between 83%-86%. The accuracy reported for the models has
little variations, which is due to the nature of the data. After
normalization, we have noticed that the accuracy improved and
ranged between 85%-90%. Finally, by applying augmentation
we can see a visible increase in accuracy, which was reported
approximately between 96%-99%. The result obtained during
our experimentation out performs the method proposed in [11].
V. CONCLUSION
In this paper, we developed the Lung Disease Classification
(LDC) system combined with advanced data normalization and
data augmentation techniques, for high-performance classifica-
tion in lung disease diagnosis. We have obtained 97% accuracy
better than the state-of-the art accuracy. This confirms that
the proposed model could be used for the diagnosis of lung
diseases with lung sounds in health care.
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