Covid Detection from X-RAY and CT Scans using Transfer Learning – A Study

Kumar, Shailender; Naman,; Talib, Mohd Khaidir Abu; Verma, Praduman

doi:10.1109/icais50930.2021.9395784

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…In their work of detection of COVID-19 with CT images using hybrid complex shearlet scattering networks [11], Ren et al presented a hybrid framework in which a single model was constructed by combining the complex shearlet scattering transform (CSST) and a suitable convolutional neural network. The wide residual network is also implemented in this suggested hybrid model, which performs well on sparse and locally invariant images.…”

Section: Et Al Published "Automatic Detection Of Coronavirus (Covid-1...mentioning

confidence: 99%

Diagnosis of COVID-19 from Multimodal Imaging Data Using Optimized Deep Learning Techniques

2023

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COVID-19 had a global impact, claiming many lives and disrupting healthcare systems even in many developed countries. Various mutations of the severe acute respiratory syndrome coronavirus-2, continue to be an impediment to early detection of this disease, which is vital for social well-being. Deep learning paradigm has been widely applied to investigate multimodal medical image data such as chest X-rays and CT scan images to aid in early detection and decision making about disease containment and treatment. Any method for reliable and accurate screening of COVID-19 infection would be beneficial for rapid detection as well as reducing direct virus exposure in healthcare professionals. Convolutional neural networks (CNN) have previously proven to be quite successful in the classification of medical images. A CNN is used in this study to suggest a deep learning classification method for detecting COVID-19 from chest X-ray images and CT scans. Samples from the Kaggle repository were collected to analyse model performance. Deep learning-based CNN models such as VGG-19, ResNet-50, Inception v3 and Xception models are optimized and compared by evaluating their accuracy after pre-processing the data. Because X-ray is a less expensive process than CT scan, chest X-ray images are considered to have a significant impact on COVID-19 screening. According to this work, chest X-rays outperform CT scans in terms of detection accuracy. The fine-tuned VGG-19 model detected COVID-19 with high accuracy-up to 94.17% for chest X-rays and 93% for CT scans. This work thereby concludes that VGG-19 was found to be the best suited model to detect COVID-19 and chest X-rays yield better accuracy than CT scans for the model.

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Section: Et Al Published "Automatic Detection Of Coronavirus (Covid-1...mentioning

confidence: 99%

Diagnosis of COVID-19 from Multimodal Imaging Data Using Optimized Deep Learning Techniques

2023

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“…DenseNet and its variants were utilized in a number of deep learning articles, including [31,49,54,56,62,119]. ResNet and its variants were also widely utilized, with articles [31,39,44,46,47,49,50,54,55,58,59,[66][67][68]97] mentioning them. Researchers that have employed image processing and feature extraction for COVID-19 detection can be found in the image processing section.…”

Section: Discussionmentioning

confidence: 99%

“…Sharma employed the CNN model with X-ray images in 2021 [65], but they also used VGG16 and VGG19 since they both produced superior accuracy of 0.97 compared to 0.94 for CNN, but their CNN model required less computational resources than VGG16 and VGG19. Kumar et al proposed [66] in 2021, in which they tested VGG16 and ResNet models for COVID-19 identification using CT scan and X-ray pictures, as well as CLAHE, U-Net, and CNN for image cropping and histogram equalisation. They discovered that VGG16 provided superior accuracy while ResNet was more dependable, resulting in an accuracy of 0.974 and an F1 score of 0.979.…”

Section: Methods For Deep Learning Techniquementioning

confidence: 99%

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

Appari¹,

Kanojia²

2022

HIS

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COVID-19 is a contagious respiratory illness that can be passed from person to person. Because it affects the lungs, damages blood arteries, and causes cardiac problems, COVID-19 must be diagnosed quickly. The reverse transcriptase polymerase chain reaction (RT-PCR) is a method for detecting COVID-19, but it is time consuming and labor expensive, as well as putting the person collecting the sample in danger. As a result, clinicians prefer to use CT scan and Xray images. COVID-19 classification can be done manually, however AI makes the process go faster. AI approaches include image processing, machine learning, and deep learning. An AI model is required to diagnose COVID-19, and a dataset is necessary to train that model. A dataset consists of the information from which the model is trained. This paper consists of the review of different image processing, machine learning and deep learning papers proposed by different researchers. As well as models based on deep learning and pretrained model using gradient boosting algorithm The goal of this paper is to provide information for future researchers to work with.

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“…Transfer learning aims to solve problems in the target domain using the knowledge that has been learned in the relevant domain. Many representative transfer learning methods have been put into practical applications, covering areas including text classification [1][2][3][4][5], medicine [6][7][8][9], transportation [10][11][12], recommendation systems [13][14][15][16], etc. Previous studies have shown that the model trained using the original features directly for cross-domain text classification is not ideal.…”

Section: Abbreviationsmentioning

confidence: 99%

Dual-Space Transfer Learning Based on an Indirect Mutual Promotion Strategy

Cui

Pan

et al. 2022

Int J Comput Intell Syst

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Transfer learning is designed to leverage knowledge in the source domain with labels to help build classification models in the target domain where labels are scarce or even unavailable. Previous studies have shown that high-level concepts extracted from original features are more suitable for cross-domain classification tasks, so many transfer learning methods transfer knowledge by modeling high-level concepts on the original feature space. However, there are two limitations to this method: First, learning high-level concepts directly on the original feature space will reduce the proportion of shared information contained in common features in the process of knowledge transfer bridge construction. Second, only learning multiple high-level concepts on the original feature space, the latent shared information contained in the domain-specific features cannot be targeted learned, so the latent shared information in the domain-specific features cannot be effectively used. To overcome these limitations, this paper proposes a novel method named Dual-Space Transfer Learning based on an Indirect Mutual Promotion Strategy (DSTL). The DSTL method is formalized as an optimization problem based on non-negative matrix tri-factorization. DSTL first extracts the common features between domains and constructs the common feature space. Then, the learning of the high-level concepts of the common feature space and the original feature space is integrated through an indirect promotion strategy, which can enhance the learning effect of common features and domain-specific features through the mutual help of the two feature spaces. The system test on benchmark data sets shows the superiority of the DSTL method.

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Covid Detection from X-RAY and CT Scans using Transfer Learning – A Study

Cited by 7 publications

References 10 publications

Diagnosis of COVID-19 from Multimodal Imaging Data Using Optimized Deep Learning Techniques

Diagnosis of COVID-19 from Multimodal Imaging Data Using Optimized Deep Learning Techniques

Soft computing and image processing techniques for COVID-19 prediction in lung CT scan images

Dual-Space Transfer Learning Based on an Indirect Mutual Promotion Strategy

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