HYBRID QUANTUM-CLASSICAL DEEP LEARNING MODEL FOR PREDICTION OF COVID-19 USING CHEST X-RAY IMAGES

Abstract

Medical images are difficult to collect and are full of insecurities and expensiveness.Pandemic such as COVID-19 break out suddenly and may be transferable from one person to another ,so we need to identity the victim and isolate them.Prescience of less datasets of such cases are difficult for the classical convolution model for prediction of disease.We need a high performance and accurate image classification model that assists doctor in diagnosis.The CNN layer of deep learning is also computationally complex as it need a lot of weights to train for better performance ,this increases the computational complexity of the model.Therefore,it is very necessary to develop a model which is fast,accurate and computationally efficient model.Here,we present a hybrid quantum classical convolution neural network for image classification.We run the model in simulators and different real quantum devices.We found that the hybrid model with less trainable parameters with low resolution and small training images was able to outperform the classical convolution neural network.The best hybrid quantum -classical model in this work was with accuracy of 0.9348 and 12318 trainable parameters.The best classical model was with accuracy of 0.9076.The computationally efficient model was with accuracy of 0.9239 with 2355 learn able parameters