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• W4302425268 abstract "• Proposing a CNN based deep learning system to diagnosis Covid-19 from Chest X-Ray images. The aim of this paper is to help radiologists and medical experts to identify Covid-19 and pneumonia from X-Rays which is a critical task for human being and it could be overlooked as both the disease have almost similar pixel features on X-Ray. Besides this, there is a shortage of testing kits for RT-PCR test and it takes time to identify the infection status. To overcome this, we try to design this system using deep learning as it computer aided system takes less time to make decisions and thus helps to hold up the spreading of the virus. • To conduct our research, first, we collected X-Ray images from different publicly available dataset. To make our system robust, we done some significant pre-processing on the dataset including resizing, normalization, augmentation etc. From the gathered data, we made two dataset: Dataset-1 contains normal, pneumonia and Covid-19 cases and Dataset-2 contains Covid-19 and pneumonia cases. Finally, we run our model using both datsets. • We measured the outcomes by using different statistical evaluation criteria: Accuracy, Specificity, Precision, Sensitivity and F-1 score and found that our proposed system performed well for three class classification. For three class classification, our system achieved 98.5% accuracy. In addition, the sated system is very efficient to differentiate Covid-19 patients from pneumonia as well with an accuracy of 99.6%. Finally, we applied the Gradient Class Activation Map techniques to visualize the area that the deep learning system took into account for the classification. We hope that our proposed system could be helpful for the radiologist to make clear and consistent decision for Covid-19 cases. In recent years, coronavirus (Covid-19) has evolved into one of the world’s leading life-threatening severe viral illnesses. A self-executing accord system might be a better option to stop Covid-19 from spreading due to its quick diagnostic option. Many researches have already investigated various deep learning techniques, which have a significant impact on the quick and precise early detection of Covid-19. Most of the existing techniques, though, have not been trained and tested using a significant amount of data. In this paper, we purpose a deep learning technique enabled Convolutional Neural Network (CNN) to automatically diagnose Covid-19 from chest x-rays. To train and test our model, 10,293 x-rays, including 2875 x-rays of Covid-19, were collected as a data set. The applied dataset consists of three groups of chest x-rays: Covid-19, pneumonia, and normal patients. The proposed approach achieved 98.5% accuracy, 98.9% specificity, 99.2% sensitivity, 99.2% precision, and 98.3% F1-score. Distinguishing Covid-19 patients from pneumonia patients using chest x-ray, particularly for human eyes is crucial since both diseases have nearly identical characteristics. To address this issue, we have categorized Covid-19 and pneumonia using x-rays, achieving a 99.60% accuracy rate. Our findings show that the proposed model might aid clinicians and researchers in rapidly detecting Covid-19 patients, hence facilitating the treatment of Covid-19 patients." @default.
• W4302425268 created "2022-10-06" @default.
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• W4302425268 date "2022-11-01" @default.
• W4302425268 modified "2023-09-30" @default.
• W4302425268 title "Deep viewing for the identification of Covid-19 infection status from chest X-Ray image using CNN based architecture" @default.
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• W4302425268 doi "https://doi.org/10.1016/j.iswa.2022.200130" @default.
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