Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images

Yaşar, Hüseyin; Ceylan, Murat

doi:10.1007/s12559-021-09915-9

Cited by 8 publications

(6 citation statements)

References 55 publications

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“…They tested these predictions to decrease the false-positive and false-negative numbers. It addition, they classified the pneumonia that results from COVID-19 and other types of pneumonia using a 24-layer neural network [ 29 ]. Melek et al proposed a system to distinguish between COVID-19 patients and other patients using cough sounds.…”

Section: Related Workmentioning

confidence: 99%

A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms

Said

2023

Mobile Netw Appl

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In recent decades, many infectious diseases have appeared that have negatively affected life in general and people in particular, causing many economic and human losses. Recently, many attempts have emerged to confront these diseases using computer-based technology for diagnosis, prediction, and data analysis using various techniques, the most important of which is deep learning. Previous research relied primarily on a set of images taken from the patient’s body while he was in a healthcare facility, and this is the main weakness of these studies. Not all people go to a doctor or hospital when they feel the symptoms of a disease. Hence, people moving in crowded places without knowing their health status can contribute to spreading infectious diseases quickly, and this is the issue that should be confronted. Therefore, this paper presents a people-monitoring scheme, which is based on the internet of things (IoT) technology, to predict infectious disease symptoms through people’s behavior as well as through a wireless body area network (WBAN). This scheme can predict the spread of disease by tracking the movements of infected persons. Additionally, a simple methodology for processing the data extracted from the monitoring process across a range of different computing centers is introduced. Moreover, to ensure the monitoring scheme operates in real-time, it was necessary to provide a powerful coverage model for its objects. Also, a simple COVID-19 case study is presented. Finally, the performance of the prediction model is measured using images, sounds and videos files. Furthermore, the performance of the data computing and coverage methodologies is measured using an intensive simulation environment for the IoT that was constructed using NS3 package. The results showed that the proposed scheme is able to predict the symptoms of disease and its spread with accepted level of accuracy. In addition, using a mixture of coverage tools and computing techniques is recommended.

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Section: Related Workmentioning

confidence: 99%

A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms

Said

2023

Mobile Netw Appl

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“…The working structure of the pipeline algorithm was visualized in Figure 2. Yasar and Ceylan have used the pipeline algorithm mentioned above, in their studies (Yasar and Ceylan, 2021a;Yasar and Ceylan, 2021b) for diagnosing COVID-19 disease from CT and X-ray images.…”

Section: Pipeline Algorithmmentioning

confidence: 99%

“…Similarly, when the results obtained for the two-class classification of COVID-19 pneumonia severity are examined, it is understood that improvements at the level of 1% have been achieved. In this context, it is seen that the approach of using the pipeline algorithm, which has produced successful results (Yasar and Ceylan, 2021a;Yasar and Ceylan, 2021b) in the studies of automatic diagnosis of COVID-19 disease from X-ray and CT images, has also increased the results for COVID-19 pneumonia severity classification.…”

Section: Acknowledgementsmentioning

confidence: 99%

A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline Algorithm

Yaşar,

Ceylan,

Cebeci

et al. 2023

ADCAIJ

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SARS-CoV-2 has caused a severe pandemic worldwide. This virus appeared at the end of 2019. This virus causes respiratory distress syndrome. Computed tomography (CT) imaging provides important radiological information in the diagnosis and clinical evaluation of pneumonia caused by bacteria or a virus. CT imaging is widely utilized in the identification and evaluation of COVID-19. It is an important requirement to establish diagnostic support systems using artificial intelligence methods to alleviate the workload of healthcare systems and radiologists due to the disease. In this context, an important study goal is to determine the clinical severity of the pneumonia caused by the disease. This is important for determining treatment procedures and the follow-up of a patient’s condition. In the study, automatic COVID-19 severity classification was performed using three-class (mild, moderate, and severe) and two-class (non-severe and severe). In the study, deep learning models were used for classification. Also, CT images were utilized as radiological images. A total of 483 COVID-19 CT-image slices, 267 mild, 156 moderate, and 60 severe, were used. These images and labels were used directly for the three classifications. In the two-class classification, the mild and moderate images were accepted as non-severe. A total of eight classifications were made with convolutional neural network (CNN) architectures. These architectures are MobileNetv2, ResNet101, Xception, Inceptionv3, GoogleNet, EfficientNetb0, DenseNet201, and DarkNet53. In the study, the results of the top four CNN architectures with the best performance were combined using a pipeline algorithm. In this way, it is seen that significant improvements have been achieved in the results of the study. Before using the pipeline algorithm for the three-class classification, the results of weighted recall-sensitivity (SNST), specificity (SPCF), accuracy (ACCR), F-1 score (F-1), area under the receiver operating characteristic curve (AUC), and overall ACCR were obtained: 0.7785, 0.8351, 0.8299, 0.7758, 0.9112 and 0.7785, respectively. After using the pipeline algorithm for the three-class classification, the results of these parameters were obtained: 0.8095, 0.8555, 0.8563, 0.8076, 0.9089, and 0.8095, respectively. Before using the pipeline algorithm for the two-class classification, the results of SNST, SPCF, ACCR, F-1, and AUC were obtained: 0.9740, 0.8500, 0.9482, 0.9703, and 0.9788, respectively. After using the pipeline algorithm for the two-class classification, the results of these parameters were obtained: 0.9811, 0.8333, 0.9627, 0.9788, and 0.9851, respectively.

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“…Therefore, the time required to generate results for an image using the pipeline algorithm is equal to the sum of the individual result generation times of the original image and the LBP (or LE) feature image. This pipeline algorithm was used by Yasar and Ceylan [49,50] in the two-class classification of COVID-19 and healthy X-ray and CT images and provided successful results.…”

Section: Pipeline Algorithm Used Within the Scope Of The Studymentioning

confidence: 99%

A Novel Comparative Study for Automatic Three-Class and Four-Class Covid-19 Classification on X-Ray Images Using Deep Learning

Yaşar

Ceylan²

2022

MJCS

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The contagiousness rate of the COVID-19 virus, which was evaluated to have been transmitted from an animal to a human during the last months of 2019, is higher than the MERS-Cov and SARS-Cov viruses originating from the same family. The high rate of contagion has caused the COVID-19 virus to spread rapidly to all countries of the world. It is of great importance to be able to detect cases quickly in order to control the spread of the COVID-19 virus. Therefore, the development of systems that make automatic COVID-19 diagnoses using artificial intelligence approaches based on Xray, CT scans, and ultrasound images are an urgent and indispensable requirement. In order to increase the number of X-ray images used within the study, a mixed data set was created by combining eight different data sets, thus maximizing the scope of the study. In the study, a total of 9,667 X ray images were used, including 3,405 of COVID-19 samples, 2,780 of bacterial pneumonia samples, 1,493 of viral pneumonia samples and 1,989 of healthy samples. In this study, which aims to diagnose COVID-19 disease using X-ray images, automatic classification has been performed using two different classification structures: COVID-19 Pneumonia/Other Pneumonia/Healthy and COVID-19 Pneumonia/Bacterial Pneumonia/Viral Pneumonia/Healthy. Convolutional Neural Networks (CNNs), a successful deep learning method, were used as a classifier within the study. A total of seven CNN architectures were used: Mobilenetv2, Resnet101, Googlenet, Xception, Densenet201, Efficientnetb0, and Inceptionv3 architectures. The classification results were obtained from the original X-ray images, and the images were obtained by using Local Binary Pattern and Local Entropy. Then, new classification results were calculated from the obtained results using a pipeline algorithm. Detailed results were obtained to meet the scope of the study. According to the results of the experiments carried out, the three most successful CNN architectures for both three-class and four class automatic classification were Densenet201, Xception, and Inceptionv3, respectively. In addition, it is understood that the pipeline algorithm used in the study is very useful for improving the results. The study results show that up to an improvement of 1.57% were achieved in some comparison parameters.

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Deep Learning–Based Approaches to Improve Classification Parameters for Diagnosing COVID-19 from CT Images

Cited by 8 publications

References 55 publications

A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms

A Large-Scale IoT-Based Scheme for Real-Time Prediction of Infectious Disease Symptoms

A Novel Study for Automatic Two-Class and Three-Class COVID-19 Severity Classification of CT Images using Eight Different CNNs and Pipeline Algorithm

A Novel Comparative Study for Automatic Three-Class and Four-Class Covid-19 Classification on X-Ray Images Using Deep Learning

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