Self-assessment and deep learning-based coronavirus detection and medical diagnosis systems for healthcare

Kashif Naseer Qureshi, adi alhudhaif, Moazam Ali, Maria Ahmed Qureshi, Gwanggil Jeon

Research output: Contribution to journalJournal articlepeer-review


Coronavirus is one of the serious threat and challenge for existing healthcare systems. Several prevention methods and precautions have been proposed by medical specialists to treat the virus and secure infected patients. Deep learning methods have been adopted for disease detection, especially for medical image classification. In this paper, we proposed a deep learning-based medical image classification for COVID-19 patients namely deep learning model for coronavirus (DLM-COVID-19). The proposed model improves the medical image classification and optimization for better disease diagnosis. This paper also proposes a mobile application for COVID-19 patient detection using a self-assessment test combined with medical expertise and diagnose and prevent the virus using the online system. The proposed deep learning model is evaluated with existing algorithms where it shows better performance in terms of sensitivity, specificity, and accuracy. Whereas the proposed application also helps to overcome the virus risk and spread.

Original languageEnglish
JournalMultimedia Systems
StateAccepted/In press - 2021


  • Application
  • Challenges
  • Coronavirus
  • Deep learning
  • Detection
  • Diagnosis
  • Disease
  • Healthcare
  • Systems
  • Technologies


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