Artificial Intelligence Defogging Algorithm for Chest CT Scan Images for Post-Covid-19 Patients Infected with H3N2 Virus


  • Aravind Jadhav Department of E&CE, Angadi Institute of Technology and Management, Belagavi, Karnataka, India, Affiliated VTU Belagavi, karnataka, India
  • Sanjay Pujari Department of E&CE, KLE College of Engineering & Technology, Chikodi, Belagavi, Karnataka, India


Artificial Intelligence, Human Machine Interface, CNN, deep learning, covid-19, H3N2


On the verge of Covid-19 pandemic, new Influenza a H3N2 variant virus is immerging throughout India. It is crucial to identify the impact of post-covid-19 patients and H3N2 infected patients. As all comes to the lung health, the primary element for diagnosis is the CT scan image and MRI of patient regardless of type of the virus. The medicinal strategy is similar due to similar indications of lung infection status in CT images of lungs. The additional and painful symptom of H3N2 infected post-covid-19 patient is the blood in the cough. Doctors need to know the level of infection of lungs in a clear way by defogging the blood clots in the cough present in the lungs or respiratory track. Hence, this paper presents the research to identify with defogging of lung CT image analysis by capturing the more prominent area of lung CT images more clear vision. The proposed research presents the new algorithm named ‘AI-Defogging’ using artificial intelligence to automatically regenerate the augmented pixels out of lung CT images.


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How to Cite

Jadhav, A. ., & Pujari, S. . (2023). Artificial Intelligence Defogging Algorithm for Chest CT Scan Images for Post-Covid-19 Patients Infected with H3N2 Virus. International Journal of Intelligent Systems and Applications in Engineering, 12(2), 119–128. Retrieved from



Research Article