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A Deep Learning and Grad-CAM based Color Visualization Approach for Fast Detection of COVID-19 Cases using Chest X-ray and CT-Scan Images.
Chaos Solitons Fractals ; : 110190, 2020 Aug 07.
Article in English | MEDLINE | ID: covidwho-696427
The world is suffering from an existential global health crisis known as the COVID-19 pandemic. Countries like India, Bangladesh, and other developing countries are still having a slow pace in the detection of COVID-19 cases. Therefore, there is an urgent need for fast detection with clear visualization of infection is required using which a suspected patient of COVID-19 could be saved. Therefore, there is an urgent need for fast detection and clear visualization of infection is required. In the recent technological advancements, the fusion of deep learning classifiers and medical images provides more promising results corresponding to traditional RT-PCR testing while making detection and predictions about COVID-19 cases with increased accuracy. In this paper, we have proposed a deep transfer learning algorithm that accelerates the detection of COVID-19 cases by using X-ray and CT-Scan images of the chest. It is because, in COVID-19, initial screening of chest X-ray (CXR) may provide significant information in the detection of suspected COVID-19 cases. We have considered three datasets known as 1) COVID-chest X-ray, 2) SARS-COV-2 CT-scan, and 3) Chest X-Ray Images (Pneumonia). In the obtained results, the proposed deep learning model can detect the COVID-19 positive cases in  ≤  2 seconds which is faster than RT-PCR tests currently being used for detection of COVID-19 cases. We have also established a relationship between COVID-19 patients along with the Pneumonia patients which explores the pattern between Pneumonia and COVID-19 radiology images. In all the experiments, we have used the Grad-CAM based color visualization approach in order to clearly interpretate the detection of radiology images and taking further course of action.





Full text: Available Collection: International databases Database: MEDLINE Type: Article Type of study: Diagnostic study / Prognostic study Language: English Journal: Chaos Solitons Fractals Clinical aspect: Prediction / Prognosis Year: 2020