Abstract
Coronavirus disease 2019 (COVID-19) pandemic has become a major threat to the entire world and severely affects the health and economy of many people. It also causes the lot of other diseases and side effects after taking treatment for COVID. Early detection and diagnosis will reduce the community spread as well as saves the life. Even though clinical methods are available, some of the imaging methods are being adopted to fix the disease. Recently, several deep learning models have been developed for screening COVID-19 using computed tomography (CT) images of the chest, which plays a potential role in diagnosing, detecting complications, and prognosticating coronavirus disease. However, the performances of the models are highly affected by the limited availability of samples for training. Hence, in this work, deep convolutional generative adversarial network (DCGAN) has been proposed and implemented which automatically discovers and learns the regularities from input data so that the model can be used to generate requisite samples. Further, the hyperparameters of DCGAN such as number of neurons, learning rate, momentum, alpha, and dropout probability have been optimized by using genetic algorithm (GA). Finally, deep convolutional neural network (CNN) with various optimizers is implemented to predict COVID-19 and non-COVID-19 images which assist radiologists to increase diagnostic accuracy. The proposed deep CNN model with GA optimized DCGAN exhibits an accuracy of 94.50% which is higher than the pre-trained models such as AlexNet, VggNet, and ResNet.
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Acknowledgements
Authors would like to thank UGC, New Delhi, for the financial support received under UGC-SAP No. F.5-6/2018/DRS-II (SAP-II).
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Thangavel, K., Sasirekha, K. (2022). Classification of COVID-19 Chest CT Images Using Optimized Deep Convolutional Generative Adversarial Network and Deep CNN. In: Saraswat, M., Sharma, H., Balachandran, K., Kim, J.H., Bansal, J.C. (eds) Congress on Intelligent Systems. Lecture Notes on Data Engineering and Communications Technologies, vol 111. Springer, Singapore. https://doi.org/10.1007/978-981-16-9113-3_27
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DOI: https://doi.org/10.1007/978-981-16-9113-3_27
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