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2022 ◽  
Vol 28 ◽  
pp. e00461
Alvaro José Gomes de Faria ◽  
Sérgio Henrique Godinho Silva ◽  
Renata Andrade ◽  
Marcelo Mancini ◽  
Leônidas Carrijo Azevedo Melo ◽  

Farah Flayeh Alkhalid ◽  
Abdulhakeem Qusay Albayati ◽  
Ahmed Ali Alhammad

The main important factor that plays vital role in success the deep learning is the deep training by many and many images, if neural networks are getting bigger and bigger but the training datasets are not, then it sounds like going to hit an accuracy wall. Briefly, this paper investigates the current state of the art of approaches used for a data augmentation for expansion the corona virus disease 2019 (COVID-19) chest X-ray images using different data augmentation methods (transformation and enhancement) the dataset expansion helps to rise numbers of images from 138 to 5520, the increasing rate is 3,900%, this proposed model can be used to expand any type of image dataset, in addition, the dataset have used with convolutional neural network (CNN) model to make classification if detected infection with COVID-19 in X-ray, the results have gotten high training accuracy=99%

2022 ◽  
Vol 147 ◽  
pp. 107547
Hitoshi Ozaki ◽  
Yoshihito Akao ◽  
Minh Quang Le ◽  
Hiroshi Kawakami ◽  
Jippei Suzuki ◽  

2022 ◽  
Vol 236 ◽  
pp. 111510
Kejun Chen ◽  
Alexandra Bothwell ◽  
Harvey Guthrey ◽  
Matthew B. Hartenstein ◽  
Jana-Isabelle Polzin ◽  

Diagnosis of COVID-19 pneumonia using patients’ chest X-Ray images is new but yet important task in the field of medicine. Researchers from different parts of the globe have developed many deep learning models to classify COVID-19. The performance of feature extraction and classifier plays a vital role in the recognizing the different patterns in the image. The pivotal process is the extraction of optimum features from the chest X-Ray images. The main goal of this study is to design an efficient hybrid algorithm that integrates the robustness of MobileNet (using transfer learning approach) to extract features and Support Vector Machine (SVM) to classify COVID-19. Experiments were conducted to test the proposed algorithm and it was found to have a high classification accuracy of 95%.

Rajeev Kumar Gupta ◽  
Nilesh Kunhare ◽  
Rajesh Kumar Pateriya ◽  
Nikhlesh Pathik

The novel Covid-19 is one of the leading cause of death worldwide in the year 2020 and declared as a pandemic by world health organization (WHO). This virus affecting all countries across the world and 5 lakh people die as of June 2020 due to Covid-19. Due to the highly contagious nature, early detection of this virus plays a vital role to break Covid chain. Recent studies done by China says that chest CT and X-Ray image may be used as a preliminary test for Covid detection. Deep learning-based CNN model can use to detect Coronavirus automatically from the chest X-rays images. This paper proposed a transfer learning-based approach to detect Covid disease. Due to the less number of Covid chest images, we are using a pre-trained model to classify X-ray images into Covid and Normal class. This paper presents the comparative study of a various pre-trained model like VGGNet-19, ResNet50 and Inception_ResNet_V2. Experiment results show that Inception_ResNet_V2 gives the better result as compare to VGGNet and ResNet model with training and test accuracy of 99.26 and 94, respectively.

2022 ◽  
Vol 210 ◽  
pp. 105886
Suman Budhathoki ◽  
Jasmeet Lamba ◽  
Puneet Srivastava ◽  
Colleen Williams ◽  
Francisco Arriaga ◽  

2023 ◽  
Vol 1 (1) ◽  
pp. 1
Nilesh Bahadure ◽  
S. Rajasoundaran ◽  
A.V. Prabu ◽  
V. Pandimurugan ◽  

Geoderma ◽  
2022 ◽  
Vol 409 ◽  
pp. 115649
G. Shrestha ◽  
R. Calvelo-Pereira ◽  
P. Roudier ◽  
A.P. Martin ◽  
R.E. Turnbull ◽  

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