COVID-19 Image Repository

Winther, Hinrich B.; Laser, H.; Gerbel, Svetlana; Maschke, Sabine; Hinrichs, Jan B.; Vogel‐Claussen, Jens; Wacker, Frank; Höper, Marius M.; Meyer, B

doi:10.25835/0090041

Cited by 7 publications

(2 citation statements)

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“…After this stage, the image dimensions were rearranged by the CNN architecture to be used. [24] 462 X X X Wang et al [25,26] 35 X X X Winther et al [27,28] 243 X X X Desai et al [29,30] 253 X X X Vayá et al [31,32] 2,412 X X X Kermany et al [33,34] X 1,583 2,780 1,493 Montgomery [35] X 80 X X Shenzhen [35] X 326 X X Total The experiments performed in the study were carried out according to the standard 4-fold cross-validation procedure. In this context, numerical information regarding the division of images for cross validation is given in Table 4.…”

Section: Used Datamentioning

confidence: 99%

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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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Section: Used Datamentioning

confidence: 99%

“…Third, 243 COVID-19 X-ray images from the data set created by Winther et al[27,28] were used. The COVID-19 X-ray image collection process was completed by taking 253 images from the data set created by Desai et al[29,30] and 2,412 images from the data set created by Vayá et al[31,32].…”

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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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A survey on deep learning models for detection of COVID-19

Mozaffari

Amirkhani

Shokouhi

2023

Neural Comput & Applic

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The spread of the COVID-19 started back in 2019; and so far, more than 4 million people around the world have lost their lives to this deadly virus and its variants. In view of the high transmissibility of the Corona virus, which has turned this disease into a global pandemic, artificial intelligence can be employed as an effective tool for an earlier detection and treatment of this illness. In this review paper, we evaluate the performance of the deep learning models in processing the X-Ray and CT-Scan images of the Corona patients’ lungs and describe the changes made to these models in order to enhance their Corona detection accuracy. To this end, we introduce the famous deep learning models such as VGGNet, GoogleNet and ResNet and after reviewing the research works in which these models have been used for the detection of COVID-19, we compare the performances of the newer models such as DenseNet, CapsNet, MobileNet and EfficientNet. We then present the deep learning techniques of GAN, transfer learning, and data augmentation and examine the statistics of using these techniques. Here, we also describe the datasets introduced since the onset of the COVID-19. These datasets contain the lung images of Corona patients, healthy individuals, and the patients with non-Corona pulmonary diseases. Lastly, we elaborate on the existing challenges in the use of artificial intelligence for COVID-19 detection and the prospective trends of using this method in similar situations and conditions. Supplementary Information The online version contains supplementary material available at 10.1007/s00521-023-08683-x.

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Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia

et al. 2022

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The emergence of COVID-19 as a global pandemic forced researchers worldwide in various disciplines to investigate and propose efficient strategies and/or technologies to prevent COVID-19 from further spreading. One of the main challenges to be overcome is the fast and efficient detection of COVID-19 using deep learning approaches and medical images such as Chest Computed Tomography (CT) and Chest X-ray images. In order to contribute to this challenge, a new dataset was collected in collaboration with “S.E.S Hospital Universitario de Caldas” (https://hospitaldecaldas.com/) from Colombia and organized following the Medical Imaging Data Structure (MIDS) format. The dataset contains 7,307 chest X-ray images divided into 3,077 and 4,230 COVID-19 positive and negative images. Images were subjected to a selection and anonymization process to allow the scientific community to use them freely. Finally, different convolutional neural networks were used to perform technical validation. This dataset contributes to the scientific community by tackling significant limitations regarding data quality and availability for the detection of COVID-19.

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COVID-19 Image Repository

Cited by 7 publications

References 0 publications

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

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

A survey on deep learning models for detection of COVID-19

Cov-caldas: A new COVID-19 chest X-Ray dataset from state of Caldas-Colombia

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