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• W3108492058 abstract "The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019 (COVID-19). The usage of sophisticated artificial intelligence technology (AI) and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages. In this research, the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia, reported COVID-19 disease, and normal cases. The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures. Transfer Learning technique has been implemented in this work. Transfer learning is an ambitious task, but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images. The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection. Since all diagnostic measures show failure levels that pose questions, the scientific profession should determine the probability of integration of X-rays with the clinical treatment, utilizing the results. The proposed model achieved 96.73% accuracy outperforming the ResNet50 and traditional Resnet18 models. Based on our findings, the proposed system can help the specialist doctors in making verdicts for COVID-19 detection." @default.
• W3108492058 created "2020-12-07" @default.
• W3108492058 creator A5019248591 @default.
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• W3108492058 date "2021-01-01" @default.
• W3108492058 modified "2023-10-16" @default.
• W3108492058 title "Automatic Detection of COVID-19 Using Chest X-Ray Images and Modified ResNet18-Based Convolution Neural Networks" @default.
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• W3108492058 doi "https://doi.org/10.32604/cmc.2020.013232" @default.
• W3108492058 hasPublicationYear "2021" @default.
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