Development of the new Aquicnn algorithm for an augmentation of CT scans images for COVID-19 Patients

Abstract

The world is restoring life balance after the global Covid-19 pandemic. This situation, giving birth to new problems, arose as an outcome of pre-and post pandemic scenarios. Healthcare system was under tremendous burden during this pandemic. Government bodies, scientists, drug discovery and drug registration were working in cooperation to fight the situation and to save lives. Out of all such activities, one healthcare domain is a key player, and that is the radiological department of hospitals. As discovery made those Covid-19 effects on lungs, the pressure on CT scan activities rose. To generate a CT scan quickly and to diagnose lung condition is the need of hour. Furthermore, that became a challenge for early detection of lung conditions. Hence, this paper presents the proposed research to develop iterative techniques using deep learning computation. Paper presents the proposed lung image acquisition and augmentation algorithm developed using a convolution neural network named “AquiCNN”. This proposed algorithm will be useful for quick and enhanced lung CT image analysis.