Classification of Covid-19 patients using efficient Fine-tuned Deep learning DenseNet Model

Bohmrah, Maneet Kaur; Kaur, Harjot

As COVID-19 pandemic caused completely spoils the livings, almost more than one year passed, still lives were not on the track. It is important to diagnose the COVID-19 patients earlier and provide the prompt treatment. The Convolutional Neural Network (CNN), a deep neural network that specializes in image processing and image classification. In this paper, a fine tuned DenseNet201 model was proposed which is used to classify Chest X ray images. Firstly, different DenseNet121, DenseNet169 and DenseNet201 model trained and tested on the same dataset. With the experiment, it is observed that DenseNet201 model performs well as compared to other dense models. Furthermore, DenseNet201 experiments over different optimizers and it is noticed that RMSprop, Adagrad and Adamax performs better. Proposed model achieves accuracy of 95.2% as compared to other models. We experimentally determine that RMSprop optimizer with DenseNet201 produces better results as similar to Adam and Adamax widely used optimizers.