=Paper=
{{Paper
|id=Vol-2872/paper05
|storemode=property
|title=Coronavirus (Covid-19) Classification Using CT Images by Machine Learning Methods
|pdfUrl=https://ceur-ws.org/Vol-2872/paper05.pdf
|volume=Vol-2872
|authors=Mucahid Barstugan,Umut Ozkaya,Saban Ozturk
|dblpUrl=https://dblp.org/rec/conf/rtacsit/BarstuganOO21
}}
==Coronavirus (Covid-19) Classification Using CT Images by Machine Learning Methods==
https://ceur-ws.org/Vol-2872/paper05.pdf
Coronavirus (Covid-19) Classification Using CT Images by
Machine Learning Methods
Mücahid Barstuğana, Umut Özkayaa, and Şaban Öztürkb
a
Konya Technical University, Electrical and Electronics Engineering, Konya, 42250, Turkey
b
Amasya University, Electrical and Electronics Engineering, Amasya, 05000, Turkey
Abstract
This study detected the Coronavirus (COVID-19) disease by implementing artificial learning
methods. Coronavirus disease occurs in the lungs and can cause death. The detection process
was performed on chest Computed Tomography (CT) images. The training process was
implemented by using 32x32 patches that were obtained from CT images. This study includes
three phases: The first phase classifies patches by the SVM algorithm without implementing
the feature extraction methods. The second phase extracts features on patches by using Grey
Level Co-occurrence Matrix (GLCM), Grey Level Run Length Matrix (GLRLM), Grey-Level
Size Zone Matrix (GLSZM), Discrete Wavelet Transform (DWT), Fast Fourier Transform
(FFT), and Discrete Cosine Transform (DCT) methods and classifies the features extracted. The
third phase uses Convolutional Neural Networks (CNN) method to classify the patches. 10-fold
cross-validation is implemented in the classification process. The sensitivity, specificity,
accuracy, precision, and F-score metrics measure the classification performance. The highest
classification accuracy was achieved as 99.15% by the CNN method during the training
process. The classification structure, which has the highest classification accuracy, was used
during the test performance and had 80.21% mean sensitivity rate, which is the COVID
detection performance, on 727 test images.
Keywords 1
Classification, coronavirus, COVID-19, CT images, deep Learning, feature extraction, machine
learning
1. Introduction COVID-19 disease has its characteristics.
Therefore, clinical experts know the
characteristics and need lung CT images to
COVID-19 disease occurred at the end of
diagnose the COVID-19 in the early phase. The
2019 at Wuhan region of China. COVID-19
serial CT examinations help clinical experts to
disease shows fever, cough, fatigue, and
understand the occurrence, development, and
myalgias in the human body during the early
prognosis of the disease. CT imaging can be
phases [1]. The patients have abnormal
sorted into four stages: early stage, progressive
situations in their CT chest images. The
stage, severe stage, and dissipative stage [2].
respiratory problems, heart damages, and
Chest CT imaging modality is one of the key
secondary infection situations were observed as
elements during the diagnose of suspected
complications of the disease. The findings show
patients [3]. A total of 91,636,996 cases have
that the COVID-19 virus spreads from person
been diagnosed with COVID-19 infection;
to person. The infected person needs to be
65,524,142 patients have recovered, and
treated in the intensive care unit. Infected
1,961,037 patients have died by January 12,
people have serious respiratory problems. The
2021.
CT images of the infected people show that
Proccedings of RTA-CSIT 2021, May 2021, Tirana, Albania
EMAIL: mbarstugan@ktun.edu.tr (A. 1); uozkaya@ktun.edu.tr A.
2); saban.ozturk@amasya.edu.tr (A. 3)
© 2021 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
The development of computer vision The study proposed a deep learning-based CT
systems supports medical applications such as diagnosis system to identify COVID-19
increasing the image quality, organ patients. The proposed system had 94%
segmentation, and organ texture classification. classification accuracy. Prabira and Behera [10]
The analysis of time series and tumor proposed a method to detect infected patients
characteristics [4], the segmentation and by using X-ray scans. The dataset consisted of
detection [5] of tumor modules are some of the 100 X-ray images. The method classified X-ray
machine learning applications in biomedical images with the SVM algorithm by using deep
image processing field. features. The proposed method, which was
In the literature, there are not a detailed obtained as ResNet50+SVM, has 91.41%
study and dataset on coronavirus disease. Xu et classification performance on the MCC metric.
al. [6] classified CT images of COVID-19 into There are also some studies, which used lung
three classes as COVID-19, Influenza-A viral X-ray scans [11-14]. Besides, some studies [15,
pneumonia, and healthy cases. They obtained 16] used clinical blood test results.
images from the hospitals in Zhejiang region of This study created patches from 202 CT
China. The dataset consisted of a total of 618 images and the samples were labeled as
images, which includes 219 images from 110 infected/non-infected. The patches were
patients with COVID-19, 224 images of 224 sampled on lung and infected areas. Three
patients with Influenza-A viral pneumonia, and different phases were used during the
175 images of 175 healthy people. Their study classification of the coronavirus images. The
classified the images with a 3D-dimensional findings showed that the highest classification
deep learning model and achieved an 87.6% accuracy was assessed by automatic feature
overall classification accuracy. Shan et al. [7] extraction on the images.
implemented a deep learning-based system for This paper is organized as follows. Section
segmenting and quantification of the infected 2 analyses the images statistically and visually.
regions as well as the entire lung on chest CT Section 3 briefly explains the feature extraction
images. They used 249 COVID-19 patients and and classification methods. Section 4 presents
300 new COVID-19 patients for validation in the classification results. Section 5 concludes
their study. The study obtained the Dice the results.
similarity coefficient as 91.6%. The regular
delineation system often takes 1 to 5 hours; 2. Material
however, their proposed system reduced the
delineation time to four minutes. Wang et al. [8]
studied 453 CT images of pathogen-confirmed The dataset was taken from [17]. There are
COVID-19 cases along with previously two types of dataset, which were labeled by
experts, in [17]. The first dataset has 100
diagnosed with typical viral pneumonia. Their
different CT images. The second dataset has
study used the inception migration-learning
model to create the algorithm. The study 829 CT images, which were obtained from nine
proposed achieved 82.9% validation accuracy patients. The second dataset has non-covid
images, also. Table 1 presents the original
and 73.1% test accuracy. Ying et al. [9] used the
dataset and patch dataset created.
chest CT images, which were obtained from
two different hospitals in China, of 88 patients.
Table 1
The features of patch dataset
Dataset Number Number of Number of Number of Number of infected Number of non-infected
of images covid images training images test images training patches training patches
Dataset 1 100 100 70 30 28047 8843
Dataset 2 829 372 132 697 31307 25670
202 images were used to obtain the training images, the grey levels are similar to non-
patches. 727 images were used for the test infected regions. Also, the grey-level diversion
stage. The images in the dataset have acquired changes dramatically according to the CT tool.
from different CT tools. This situation makes Figure 1 shows the infected areas in images that
the classification process difficult. In some were acquired from different CT tools.
patches were extracted as the FOS features. The
SVM [26] method classified all features.
During the classification process, the 10-fold
cross-validation method was used. In the third
phase, a deep learning structure was used to
classify the patches. Figure 3 shows the three
phases stages of the classification process.
Figure 1: The sample images
3. Method
Figure 3: The classification processes for three
phases
This study classifies the patches into two
classes. The first class was created by the
patches that were sampled in non-infected
areas. The second class was created by the 3.1. The convolutional neural
patches that were sampled in infected areas. networks
Figure 2 shows the sample images of infected
areas.
CNN is a biologically-inspired multi-layer
perceptron (MLP) structure, which was
originated by Hubel and Wiesel who worked on
the mammalian visual cortex [27]. Fukushima
[28] introduced CNN in 1980, then LeCun et al.
[29] progressed in 1989 with a learning deep
network study.
CNN has a convolution layer and automatic
learning capabilities, which provide widely
usage in image classification, object detection,
and visual tracking applications. The progress
in hardware and parallelization increases this
Figure 2: Sample patches for infected and non-
intensive use of CNN [30]. Before the progress,
infected classes the training process has taken months. After the
progress, the training takes several days. The
This study consists of three phases. In the CNN structure includes one or more
first phase, the patches were transformed into convolution layers, pooling layers, ReLUs, and
vectors and the SVM classified the vectors. In softmax regression. The convolutional layer
the second phase, six different feature reduces the computational cost by reducing the
extraction methods as Grey Level Co- number of image parameters. The pooling layer
occurrence Matrix (GLCM) [18-20], Grey is often used to reduce matrix size without
Level Run Length Matrix (GLRLM) [21], Grey losing important features. The ReLU is an
Level Size Zone Matrix (GLSZM) [22] activation function and generally used
extracted the features. The First Order Statistics following convolution or pooling layers.
(FOS) features were obtained on patches and Finally, the softmax layer is used for the output
the transform images of Discrete Wavelet value regression. Figure 4 shows the CNN
Transform (DWT) [23], Fast Fourier Transform structure used in this study.
(FFT) [24], and Discrete Cosine Transform
(DCT) [25]. The mean, variance, skewness,
kurtosis, energy, and entropy values of the
Figure 4: The CNN structure used in Phase 3
GB DDR4 RAM, and NVIDIA GeForce GTX
1080 graphic card.
4. Experimental Results
This study presents a coronavirus 4.1. The classification results of
classification in three phases. Phase 1 classified the training process
the patches without feature extraction. Phase 2
implemented the feature extraction process on The dataset for training consists of 202 CT
all patches and classified the features extracted. images. The test set consists of 727 images.
Phase 3 used the CNN method to classify the 59354 infected and 34513 non-infected patches
patches. Five different evaluation metrics. were obtained on 202 training images. These
These metrics are sensitivity (SEN), specificity patches were classified by using three phases.
(SPE), accuracy (ACC), precision (PRE), and Table 2 presents the classification results
F-score. obtained.
All experiments were trained on a computer
with Intel Core i7-8700K CPU (3.7 GHz), 32
Table 1
The features of patch dataset
Evaluation Metrics (mean (%) ± std)
Feature Classifier Feature
Phase SEN SPE ACC PRE F-score
Extraction Structure Number
Phase 1 x SVM 1024 51.85±1.8 61.92±3.1 56.88±1.3 57.7±1.7 54.59±1.2
FOS-SVM 6 95.32±0.4 98.12±0.3 97.09±0.4 96.72±0.2 96.01±0.2
GLCM-SVM 19 13.22±0.6 100 68.09±0.2 100 23.35±0.8
Manual GLRLM-SVM 7 44.85±0.4 93.72±0.4 75.75±0.3 80.62±1.1 57.63±0.4
Phase 2 Feature GLSZM-SVM 13 2.92±0.2 100 64.31±0.1 100 5.7±0.3
Extraction DWT-FOS-SVM 24 65.25±0.6 92.5±0.4 82.48±0.3 83.5±0.7 73.25±0.4
FFT-FOS-SVM 6 65.83±1.1 90.6±0.4 81.5±0.4 80.3±0.7 72.3±
DCT-FOS-SVM 6 64.54±0.9 89.96±0.3 80.61±0.3 78.9±0.4 70.99±0.6
Table 2 shows that the classification layer, pooling layer, and the softmax layer, was
accuracy was obtained as 56.88% in Phase 1. used. Table 3 shows the highest results for
The best classification performance in Phase 2 Phase 3.
was obtained with the FOS-SVM method as
97.09% with six features. The lowest Table 3
performances were obtained with the GLSZM- The classification results for Phase 3
SVM structure, which detected infected patches
Feature Extraction Classifier Structure Accuracy (%)
with too low performance as a 2.92%
sensitivity rate. The results show that extracting Automatic Feature
CNN 99.51
features increase the classification Extraction
performance. In Phase 3, a CNN structure,
which consists of a convolution layer, ReLu
Table 3 shows that the classification Table 4
accuracy was obtained as 99.15% in Phase 3 The classification performance of test process
during the training process. Table 2 and Table
3 show that the highest classification
Evaluation Metrics (%)
performance was obtained by the CNN method.
Phase 2 achieved maximum classification Method SEN SPE ACC PRE F-score
accuracy as 97.09% with manual feature
extraction methods. Phase 3 achieved 99.15% Phase 3 80.21 99.47 98.3 90.8 85.18
classification accuracy by extracting features
automatically.
Table 4 shows that the proposed method
detects the infected images with 80.2% mean
sensitivity rate on 727 images. The non-
4.2. The classification results of infected patches are classified with a 99.47%
the test process mean specificity rate. The CNN structure has
99.15% classification performance during
This study used the trained structure, which training; however, it has 80.21% performance
has high classification accuracy, to detect the on test images, which were not used for
infected areas in images that were not used training.
during the training stage. Figure 5 presents the
test stage implemented.
5. Conlusion
COVID-19 was firstly encountered in
Figure 5: The test structure to diagnose the Wuhan region in China and has been
infected threatening the public health, trade, and world
economy. The virus shows partially similar
The trained CNN structure classified the behaviors with other viral pneumonias.
32x32 divided patches, which were not Therefore, the spreading rate of the virus made
overlapped. During the test stage, a threshold the situation difficult to be under control. CT
value was determined as “1”. If one patch is imaging results of COVID-19 show different
classified as infected, the image was classified findings according to other clinical studies.
as infected. If there were not any infected Some situations, such as the bronchiectasis,
patches in the image, the image was classified lesion swelling symptoms, and different
as non-infected. When the threshold value was shadowiness in CT images provide to diagnose
determined as “2”, the classification COVID-19, easily. This study compared
performance reduced. The reason is that some manual feature extraction-based SVM and
of images have only one patch size of infected CNN that automatically extracts features, and
area. When the threshold value was taken achieved that CNN has better performance than
bigger than “1”, even these areas were SVM method. In this study, the coronavirus
classified as infected, the image was classified image set has a different type of images, which
as non-infected. This caused classification were acquired with different CT tools.
performance to reduce. 727 test images were Therefore, different feature extraction methods
classified during test process. Table 4 presents and classifiers were implemented to find the
the mean results of test process. method that separates the infected patches.
Table 5 presents the literature studies and their
classification performances on different
coronavirus dataset.
Table 5
The literature comparison
Study Method Dataset Number of classes Performance (%)
[6] 3D-deep learning CT, 618 images 3 87.6
[8] Migration-learning CT, 453 images 2 82.9
[10] ResNet50+SVM X-ray, 100 images 2 91.41
[13] COVIDX-Net X-ray, 50 images 2 90
Blood Test, 404
[16] XG-boost 2 97
samples
Blood Test, 49
[15] Random Forest 2 95.12
samples
[30] Feature Selection and Lasso Regression Clinical Data 2 84.1
This study CNN CT, 727 images 2 80.21
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