Severity Classification of COVID-19 from Chest CT Image using Integrated Quantum-Classical Framework

Classifying the severity of medical images using the quantum-classical model with respect to the desperately required COVID-19 virus screening area. It is a pandemic concern that is having an impact on millions of people's lives. The most frequent result that involves expert evaluation in COVID-19 Computed Tomography(CT) images of the lungs is ground glass turbidity. Quantum Machine Learning (QML), is one of the fields that quantum computing has the ability to explore. These days, with so much data available, machine learning might gain from increased speed, computational complexity in which it needs a lot of weights for better performance. In this paper, exploring a machine learning issue using a quantum technique and quantum-classical hybrid model use for severity classification that perform on different input images 28*28, 32*32 and 64*64 that's run on simulator. The research laid out in this paper shows that the hybrid model succeeds over CNN in terms of low resolution image size and fewer trainable parameters. Quantum-Classical model categorizes the patient's Computed Tomography images into three severity classes: moderate, severe and critical. The best hybrid model in this study uses a 32*32 image size with accuracy of 96.82% and 3075 trainable parameters , whereas the VGG-19 model uses a 64*64 image size with 262659 trainable parameters provide accuracy of 85.97%.