HYBRID QUANTUM-CLASSICAL DEEP LEARNING MODEL FOR PREDICTION OF COVID-19 USING CHEST X-RAY IMAGES
Date
2022-09
Authors
BARAL, ANISH
Journal Title
Journal ISSN
Volume Title
Publisher
IOE Pulchowk Campus
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
Description
The World Health Organization (WHO) declared a pandemic when the coronavirus
(COVID-19), which first appeared in China in December 2019, rapidly spread
around the world [1]. During an outbreak, being able to identify COVID-19 in
a patient who is affected is crucial.
Keywords
Quantum Computing,Quanvolution Neural Network,COVID-19,Hybrid Quantum-Classical Model