Extensive COVID-19 X-Ray and CT Chest Images Dataset

El‐Shafai, Walid; El-Samie, Fathi Abd

doi:10.17632/8h65ywd2jr.3

Cited by 27 publications

(16 citation statements)

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“…The non-COVID and COVID instances of CT scan samples are included in this COVID-19 dataset [ 67 ]. A total of 5000 CT scan samples are utilized from the associated dataset for conducting this research.…”

Section: Methodsmentioning

confidence: 99%

COVID-19 identification and analysis using CT scan images: Deep transfer learning-based approach

Mohbey

Sharma²,

Kumar³

et al. 2022

Blockchain Applications for Healthcare Informatics

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Section: Methodsmentioning

confidence: 99%

COVID-19 identification and analysis using CT scan images: Deep transfer learning-based approach

Mohbey

Sharma²,

Kumar³

et al. 2022

Blockchain Applications for Healthcare Informatics

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“…To accurately evaluate the performance of the proposed model, we obtained a total of 9201 X-ray images and 2762 CT images. The datasets are available through the links in [36][37][38][39]. The X-ray images have three categories, and each category contains several images (1161 COVID-19 images, 4240 pneumonia images, and 3800 normal images).…”

Section: Dataset Descriptionmentioning

confidence: 99%

Efficient Deep-Learning-Based Autoencoder Denoising Approach for Medical Image Diagnosis

El‐Shafai¹,

El-Nabi²,

El‐Rabaie³

et al. 2022

Computers, Materials &Amp; Continua

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Effective medical diagnosis is dramatically expensive, especially in third-world countries. One of the common diseases is pneumonia, and because of the remarkable similarity between its types and the limited number of medical images for recent diseases related to pneumonia, the medical diagnosis of these diseases is a significant challenge. Hence, transfer learning represents a promising solution in transferring knowledge from generic tasks to specific tasks. Unfortunately, experimentation and utilization of different models of transfer learning do not achieve satisfactory results. In this study, we suggest the implementation of an automatic detection model, namely CADTra, to efficiently diagnose pneumonia-related diseases. This model is based on classification, denoising autoencoder, and transfer learning. Firstly, pre-processing is employed to prepare the medical images. It depends on an autoencoder denoising (AD) algorithm with a modified loss function depending on a Gaussian distribution for decoder output to maximize the chances for recovering inputs and clearly demonstrate their features, in order to improve the diagnosis process. Then, classification is performed using a transfer learning model and a four-layer convolution neural network (FCNN) to detect pneumonia. The proposed model supports binary classification of chest computed tomography (CT) images and multi-class classification of chest X-ray images. Finally, a comparative study is introduced for the classification performance with and without the denoising process. The proposed model achieves precisions of 98% and 99% for binary classification and multi-class classification, respectively, with the different ratios for training and testing. To demonstrate the efficiency and superiority of the proposed CADTra model, it is compared with some recent state-of-the-art CNN models. The achieved outcomes prove that the suggested model can help radiologists to detect pneumonia-related diseases

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“…Narin et al [2] proposed a ResNet50, Inception v3, and Inception-ResNetV2 base hybrid DL model to detect the COVID-19 infected lung through X-ray imaging. Goshal et al [11] presented a Bayesian CNN and VGG16 based framework to diagnose COVID-19 using X-ray images. They improved the cognition of DL to accurately and efficiently diagnose the COVID-19 patient.…”

Section: Related Workmentioning

confidence: 99%

“…The proposed model has been thoroughly validated by using publicly available Chest X-ray datasets 2 . The dataset 2 [11] contains both CT scan and chest x-ray images. However, we only use X-ray images to validate and calculate the accuracy of model on publicly available dataset 2 .…”

Section: Validation On Public Datasetmentioning

confidence: 99%

Lightweight Convolutional Neural Network (CNN) based COVID-19 Detection using X-ray Images

Khan¹,

Park²

2021

J Multimed Inf Syst

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In 2019, a novel coronavirus (COVID-19) outbreak started in China and spread all over the world. The countries went into lockdown and closed their borders to minimize the spread of the virus. Shortage of testing kits and trained clinicians, motivate researchers and computer scientists to look for ways to automatically diagnose the COVID-19 patient using X-ray and ease the burden on the healthcare system. In recent years, multiple frameworks are presented but most of them are trained on a very small dataset which makes clinicians adamant to use it. In this paper, we have presented a lightweight deep learning base automatic COVID-19 detection system. We trained our model on more than 22,000 dataset X-ray samples. The proposed model achieved an overall accuracy of 96.88% with a sensitivity of 91.55%.

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Extensive COVID-19 X-Ray and CT Chest Images Dataset

Cited by 27 publications

References 0 publications

COVID-19 identification and analysis using CT scan images: Deep transfer learning-based approach

COVID-19 identification and analysis using CT scan images: Deep transfer learning-based approach

Efficient Deep-Learning-Based Autoencoder Denoising Approach for Medical Image Diagnosis

Lightweight Convolutional Neural Network (CNN) based COVID-19 Detection using X-ray Images

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