Abstract
This research work proposes faster alternative ways of predicting the Coronavirus Disease 2019 (COVID-19) infection in the patient’s body. Firstly, this research work proposes a transfer learning-based solution methodology that trains the following models, VGG16, VGG19, MobileNetV2, NASnet Moblie and ResNet-50 to find out an efficient base model for Novel Corona Virus Detect-net (NCVD-net) to detect COVID-19 infected patients using chest X-rays. Among these, VGG-19 is chosen as the best base model as it outperforms the other models. Afterward, the chosen VGG-19 is transformed to VGG-19 based NCVD-net by replacing its fully connected layer with an average max pooling layer, flattened layer, three dense layers (1 x 256, 1×128, 1 x 64) with leaky ReLU activation function and one output dense layer (1 x 2) with softmax activation. Following that, VGG-19-based NCVD-net is tested against unseen chest X-rays and successfully detects COVID-19-infected patients with 99.91% accuracy. Secondly, this paper employs Federated Learning-based Novel Corona Virus Detect-net (FLNCVD-net) by considering VGG-16, VGG-19 and ResNet101 as its base model. Later, these models are distributed securely to each hospital and trained on the chest X-rays available in that hospital. Finally, the model parameters from each FLNCVD-net are securely transmitted and combined to build a global FLNCVD-net. The proposed FLNCVD-net using the above-said three base models are validated against unseen COVID-19 +ve and COVID-19 –ve chest X-ray images from Dr. Joseph Cohen’s GitHub repository and Kaggle respectively. Surprisingly, it is observed that the VGG-16-based FLNCVD-net marginally outperforms VGG-19-based FLNCVD-net.
Keywords: COVID-19, Federated Learning, Federated Learning-based Novel Corona Virus Detect-net, Novel Corona Virus Detect-net