Journal of Engineering and Sustainable
Development
http://jeasd.uomustansiriyah.edu.iq
https://doi.org/10.31272/jeasd.conf.1.12
First Online Scientific Conference for
Graduate Engineering Students
June 2020
DIAGNOSING THORAX DISEASES USING DEEP LEARNING MODELS
*Ghada
A. Shadeed 1
Mohammed A. Tawfeeq 2
Sawsan M. Mahmoud 3
1,2,3) Computer Engineering Department, Mustansiriyah University, Iraq, Baghdad
Received 00/00/0000
Accepted in revised form 00/00/0000
Abstract: Despite the availability of radiology devices in
some health care centers, thorax diseases are considered
as one of the most common health problems, especially in
rural areas. In this paper, pre-trained AlexNet and ResNet50 models are used and compared for diagnosing thorax
diseases. Chest x-ray images has been used to diagnose
thorax diseases and at first, the images cropped to extract
the rib cage part from the chest radiographs. In this study,
the Chest x-ray14 dataset is used where chest radiograph
images are inserted into the model to determine if the
person is healthy or not. In the case of an unhealthy
patient, the model can classify the disease into one of
fourteen chest diseases. The results show the ability of
ResNet-50 in achieving good performance with an
accuracy of 92.71% in classifying thorax diseases
compared with AlexNet, which has 90.80%.
Keywords: AlexNet, Chest x-ray, Deep Learning, ResNet50, Thorax diseases
Published 00/00/0000
equipment to perform an X-ray, a little number
of doctors and experts are able to diagnose chest
x-rays. Therefore, auxiliary methods should be
developed that deliver the correct diagnosis to the
patient quickly and thus give the patient the
possibility of timely treatment .
In recent years, profound education has
developed in the field of health care, especially
in disease detection. According to the success of
deep learning, many researchers have sought to
benefit from Deep Neural Networks (DNNs) for
diagnosing many diseases, including thorax
diseases on chest radiography, where numerous
reports have been published confirming high
accuracy of deep learning in diseases diagnosis.
Much research has been done using deep learning
methods to detect abnormal objects in medical
images [5] [6] [7] [8] [9] [10].
For instance, in [11] a unified Convolutional
Neural Network (CNN) framework was
proposed using weakly-supervised multi-label
classification, taking into account that pooling
strategies are different as well as various CNN's
multi-label losses. Also, in [12] the CheXNet
model, which is a model of deep learning, has
been proposed for the detection of pneumonia
1. Introduction
Chest diseases are among the most common
diseases today. More than one million people
with pneumonia enter the hospital, and about
50,000 people die annually in the United States
alone [1] [2]. In addition, according to WHO
reports, about two-thirds of the world's
population suffers from a lack of access to
radiological diagnosis [3]. This leads to the death
of patients with curable diseases in the world,
especially in rural areas [4]. Even with the
availability of the necessary medical devices and
*Corresponding Author: Ghada.shadeed@gmail.com
Work of This Research is
Licensed under CC BY
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where the area was diagnosed to identify the
disease in the image and used dense connections
[13] and batch normalization [14] to make the
optimization more possible for execution such a
model. According to their result, CheXNet
proving the capability in detect pneumonia at a
level equal to or greater than that of radiologists.
In [15] Backpropagation Neural Network
(BPNN), CNN plus Competitive Neural Network
(CPNN) were tested for the most common
disease classification in Chest x-ray. The
recognition rates were high and performance was
good where the input image has a size of 32×32
pixels according to the results that they
presented. CPNN and BPNN achieved less
generalization power than that achieved by CNN.
In [16] ChestNet is proposed to address the
diagnosis of thorax diseases on chest radiography
and was compared with three deep learning
models on the Chest x-ray14 dataset [11] using
the official patient-wise split. The recorded
results were higher than those achieved by
previous methods.
In all of the above, a number of methods were
offered to diagnose chest diseases with the help
of computer-aided diagnosis. However, the
problem of increasing the success rate of
diagnosis of diseases remains one of the most
important tasks to complete the diagnosis process
and make it more efficient .
So, the ResNet-50 model was proposed to
diagnose the chest condition based on
radiography. The diagnosis determines whether
the person is normal (no finding) or abnormal. In
the case of abnormal, the models can detect
fourteen types of chest diseases.
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models and their architectures are described.
While the results are presented in Section 3 with
corresponding discussions followed by the
conclusions in Section 4.
2. Proposed Models
In this work, the proposed models have been
pertained to three steps: the first step is image
preprocessing while in the second step a deep
learning model is applied and the last step is
disease diagnosing. In the preprocessing step,
there are three blocks: first, to obtain more
accurate and reliable data from Chest x-ray
image, the rib cage area is cropped and leave the
remaining areas in the image. This cropping
process makes the training data more useful and
thus increases the accuracy of the results and
reduces the time of training. Secondly, the
images come with different sizes; therefore, the
images must be unified within a certain size as
required by the models. Thirdly, the images are
converted to three Dimensions as shown in Fig.
1. During the training process, some details may
be forgotten in the images, so the images are
repeated to make the network remember the most
details. In addition, this process increases
network resolution. A decision is taken to
determine the type of chest disease in the last
step. In the next section, the proposed CNNs are
presented in detail.
In this paper, two deep learning models have
used that work to discover the most common
chest diseases, ResNet-50 and AlexNet, and are
compared.
The remaining of this paper is structured as
follows: In Section 2, the proposed deep learning
Figure 1. An illustration of the stages of processing
2.1. AlexNet Model
AlexNet is a type of CNNs. Its architecture is
illustrated in Fig. 2. In AlexNet's first layer, the
convolution window-shape is 11×11. In the
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second layer, the convolution window-shape is
reduced to 5×5, followed by 3×3. Maximum
pooling layers with a window-shape of 3×3 and
a stride of 2 adds after the first, second, and fifth
convolutional layers. Moreover, AlexNet has
more convolution channels. After the last
convolutional layer, there are two fully
connected layers with 4096 outputs. AlexNet
uses a simpler ReLU activation function [17]. To
retrain the model for new classification tasks, the
last fully connected layer which contents 1000
neurons has been removed and replaced by a
fully connected layer with fifteen neurons to
categorize the required fifteen classes. Where
softmax was used as an activation function.
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problems confront the network when increasing
its layers. One of these problems is when the
number of layers increases to more than 25 layers
starting with the problem of vanishing gradients,
which is formed when the gradient is very small
then the weights will not be changed effectively
and it may cause the neuronal network to stop
completely for future training [18]. So, this
model has been utilized in this work due to its
high ability to avoid many problems as well as its
efficient performance in the diagnosis of chest
diseases.
In this paper, two important strategies for
ResNet-50 is investigated together. The first
strategy, parameters were randomly initialized.
In the second strategy, the weights of the model
are initialized from a pre-trained network. In the
proposed model, the first layer requires input
images of size 224×224×3, where 3 is the number
of dimensions (ResNet-50 were designed in
order to process the three dimensions images
depending on the ImageNet [19] dataset). Then,
the images are passed to the proposed model as
the initial layer’s input, where their weights are
frozen by making learning rates equal to zero. To
retrain the model for new classification tasks, the
last fully connected layer which contents 1000
neuron has been removed and replaced by fully
connected layer with fifteen neurons to
categorize the required fifteen classes. During
training, the parameters of the frozen layers, do
not update which increases rapidly the training
speed of the model. Where softmax was used as
an activation function.
Various levels of the features of Chest x-ray
images are extracted in the first convolutional
layer as shown in Fig 4, where the learned filters
are shown at the convolution layer.
Figure 2. An illustration of the architecture of AlexNet
2.2. ResNet-50 Model
The architecture of the ResNet-50 deep learning
models is shown in Fig. 3. It consists of 50 layers.
Unlike other DNNs, the ResNet-50 model is
characterized by its ability to avoid some of the
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dataset has fifteen labels consisting of Normal
label and 14 disease labels include: Effusion,
Consolidation,
Edema,
Cardiomegaly,
Atelectasis, Emphysema, Fibrosis, Nodule,
Hernia, Mass, Infiltration, Pneumothorax,
Pleural Thickening, and Pneumonia. For the
detection task, the dataset is randomly split into
training 70% (2226 image), validation 15% (480
image), and testing 15% (476 image). The
images were saved in PNG format. The digitized
images were cropped and duplicated. For both
models, the training parameters are set as
follows: the mini-batch stochastic gradient
descent algorithm has been adopted with learning
rate of 0.0001, batch size is 10, and at the fully
connected layer the factors of the learn rate for
each of the bias and the weight is set to 10. The
ResNet-50 model achieved good diagnostic
results for all classes as shown in Fig. 6. ResNet50 model achieved the highest rate for most of
the classes than AlexNet as shown by the results
in Fig. 7. ResNet-50 model is compared with the
AlexNet model using 476 images for testing and
2226 images for training in both models where
the results show that the ResNet-50 model
outperform AlexNet model. The diagnostic
accuracy rate for the chest X-ray of ResNet-50
was 92.71% which is higher than that of AlexNet
90.80%, where for the ResNet-50 model the
highest diagnosis accuracy is 96.5% for the
edema disease class, using 86 test images and for
the AlexNet model, the highest diagnosis
accuracy is 95.35% for the edema disease class.
While for the ResNet-50 model the less accurate
classification is 85.71% of the hernia disease
class, using 70 images and for the AlexNet
model, the less accurate classification is 85.71%
of the hernia disease class. The ResNet-50 model
achieved the highest accuracy ratios for most
cases. The results showed that the ResNet-50 had
a lower error rate than an AlexNet model, which
has layers with a lower depth. The results show
Figure 3. Architecture of ResNet-50 model
(a)
(b)
Figure 4. Trained convolutional filters in the first
layer
(a) ResNet-50 model (b) AlexNet model
3. Experimental Results
The results obtained from the models were
presented in this section. The models are
implemented in Matlab 2018a using Deep
Learning Toolbox [20], working on a computer
with 8GB memory and Intel(R) Core (TM) i78550U CPU @ 1.80GHz. The models are
validated on the radiographic dataset, Chest Xray 14 released by Wang et al. [17] was used. The
dataset includes 3182 x-ray images, some
examples of this dataset are shown in Fig. 5. This
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that increasing network depth increases the
network's ability to classify.
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It can be concluded that the ResNet-50 model is
achieved high accuracy in the diagnosis of chest
radiography and records a diagnostic accuracy
rate of 92.61% for all classes, while the accuracy
rate of the AlexNet model is 90.80%. It is hoped
that these models will improve the progress of
health care and increase access to the medical
experience throughout the world when access to
skilled radiologists is limited. The results showed
that the use of deep learning methods is useful for
detecting x-ray diseases on the chest.
Figure 5. Examples from Chest x-ray14 dataset, where
the Chest x-ray14 includes 112,120 images from 30,805
patients
Acknowledgements
We commend the efforts made to make the Chest
X-ray14 dataset available, making it easier to
compare the diagnosis of 14 thorax diseases on
chest radiographs.
Conflict of interest
The authors confirm there is no conflict of
interest in publishing this article.
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Figure 6. ResNet-50 model results.
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