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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

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  • Panwar, Harsh
  • Gupta, P.K.
  • Siddiqui, Mohammad Khubeb
  • Morales-Menendez, Ruben
  • Bhardwaj, Prakhar
  • Singh, Vaishnavi

Abstract

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. 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.

Suggested Citation

  • Panwar, Harsh & Gupta, P.K. & Siddiqui, Mohammad Khubeb & Morales-Menendez, Ruben & Bhardwaj, Prakhar & Singh, Vaishnavi, 2020. "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, Elsevier, vol. 140(C).
    • Handle: RePEc:eee:chsofr:v:140:y:2020:i:c:s0960077920305865
    DOI: 10.1016/j.chaos.2020.110190
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    References listed on IDEAS

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    1. Fanelli, Duccio & Piazza, Francesco, 2020. "Analysis and forecast of COVID-19 spreading in China, Italy and France," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    2. Djilali, Salih & Ghanbari, Behzad, 2020. "Coronavirus pandemic: A predictive analysis of the peak outbreak epidemic in South Africa, Turkey, and Brazil," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    3. Panwar, Harsh & Gupta, P.K. & Siddiqui, Mohammad Khubeb & Morales-Menendez, Ruben & Singh, Vaishnavi, 2020. "Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
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    Cited by:

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    3. Muhammad Aasem & Muhammad Javed Iqbal & Iftikhar Ahmad & Madini O. Alassafi & Ahmed Alhomoud, 2022. "A Survey on Tools and Techniques for Localizing Abnormalities in X-ray Images Using Deep Learning," Mathematics, MDPI, vol. 10(24), pages 1-29, December.
    4. Muhammad Nadeem Ashraf & Muhammad Hussain & Zulfiqar Habib, 2022. "Deep Red Lesion Classification for Early Screening of Diabetic Retinopathy," Mathematics, MDPI, vol. 10(5), pages 1-26, February.
    5. Ahatsham Hayat & Preety Baglat & Fábio Mendonça & Sheikh Shanawaz Mostafa & Fernando Morgado-Dias, 2023. "Novel Comparative Study for the Detection of COVID-19 Using CT Scan and Chest X-ray Images," IJERPH, MDPI, vol. 20(2), pages 1-14, January.

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