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Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images

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  • Rajpal, Sheetal
  • Lakhyani, Navin
  • Singh, Ayush Kumar
  • Kohli, Rishav
  • Kumar, Naveen

Abstract

Coronaviruses are a family of viruses that majorly cause respiratory disorders in humans. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain of coronavirus that causes the coronavirus disease 2019 (COVID-19). WHO has identified COVID-19 as a pandemic as it has spread across the globe due to its highly contagious nature. For early diagnosis of COVID-19, the reverse transcription-polymerase chain reaction (RT-PCR) test is commonly done. However, it suffers from a high false-negative rate of up to 67% if the test is done during the first five days of exposure. As an alternative, research on the efficacy of deep learning techniques employed in the identification of COVID-19 disease using chest X-ray images is intensely pursued.

Suggested Citation

  • Rajpal, Sheetal & Lakhyani, Navin & Singh, Ayush Kumar & Kohli, Rishav & Kumar, Naveen, 2021. "Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    • Handle: RePEc:eee:chsofr:v:145:y:2021:i:c:s0960077921001028
    DOI: 10.1016/j.chaos.2021.110749
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    References listed on IDEAS

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    1. Karasu, Seçkin & Altan, Aytaç & Bekiros, Stelios & Ahmad, Wasim, 2020. "A new forecasting model with wrapper-based feature selection approach using multi-objective optimization technique for chaotic crude oil time series," Energy, Elsevier, vol. 212(C).
    2. Altan, Aytaç & Karasu, Seçkin & Bekiros, Stelios, 2019. "Digital currency forecasting with chaotic meta-heuristic bio-inspired signal processing techniques," Chaos, Solitons & Fractals, Elsevier, vol. 126(C), pages 325-336.
    3. Altan, Aytaç & Karasu, Seçkin, 2020. "Recognition of COVID-19 disease from X-ray images by hybrid model consisting of 2D curvelet transform, chaotic salp swarm algorithm and deep learning technique," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
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    Cited by:

    1. Jiachen Yang & Shukun Ma & Yang Li & Zhuo Zhang, 2022. "Efficient Data-Driven Crop Pest Identification Based on Edge Distance-Entropy for Sustainable Agriculture," Sustainability, MDPI, vol. 14(13), pages 1-11, June.
    2. Haoxuan Yu & Izni Zahidi, 2023. "Tailings Pond Classification Based on Satellite Images and Machine Learning: An Exploration of Microsoft ML.Net," Mathematics, MDPI, vol. 11(3), pages 1-14, January.

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