Abstract
In this study, a comparative study has been carried out among different Deep Learning Models like Densenet201, VGG16 net, Resnet50, and XceptionNet with the help of Transfer Learning. Each of the models were trained, first with only Actual CT Scan Images (The dataset consists of 2482 CT Scans of dimension 64 × 64 × 3, where 1252 CT scans are of patients infected by SARS-COVID-19 infection and 1230 CT scans are of patients not infected), and then with (Actual + Synthetic) images, where 3088 synthetic images of Normal CT Scan and 3683 synthetic images of COVID-19 CT Scans, were generated by training Auxiliary Classifier Generative Adversarial Network (AC GAN) with Real Images. This resulted in 9252 images containing both Real and Synthetic Data for training. Different data augmentation techniques were applied like Random Rotations, Random Horizontal Shifts, Random Vertical Shifts, Random Zoom, and Random Flips. After training all the models were compared based on different performance metrics like Accuracy, Recall, F1 Score, and Precision. These are trained and modeled to automatically detect COVID-19 infection from CT scan images. We observe a significant increase in the performance of all the models. Overall, this study augments the understanding the use of Deep Learning based Covid detection models and during COVID-19 pandemic, it can be very helpful tool for clinical practitioners and radiologists to aid them in diagnosis, quantification and follow-up of COVID-19 cases.
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Sachdev, J.S., Bhatnagar, N., Bhatnagar, R. (2021). Deep Learning Models Using Auxiliary Classifier GAN for Covid-19 Detection – A Comparative Study. In: Hassanien, A.E., et al. Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2021). AICV 2021. Advances in Intelligent Systems and Computing, vol 1377. Springer, Cham. https://doi.org/10.1007/978-3-030-76346-6_2
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