Deep transfer learning CNN based approach for COVID-19 detection

Abstract

Recently, the novel coronavirus (Covid-19) and its different variants have spread rapidly across the world. Early-stage detection of COVID-19 is a challenging task due to the limited availability of Covid testing kits to the public. Conventionally, reverse transcription-polymerase chain reaction (RT-PCR) is the reliable test for the detection of COVID-19 which is time-consuming and costly. The aim of this work is to identify the COVID-19 symptoms with the help of a deep learning algorithm using chest X-Ray images. In order to improve the quality of chest X-Ray images, authors have further modified the pre-trained model with some extra CNN layers, such as the first layer is the average pooling layer and the other two are dense layers followed by ReLU with softmax activation function. The experimental results have been carried out on publicly available chest X-Ray images of COVID-19 to mark COVID-19 patients as positive and negative datasets. For evaluation purpose, we have used benchmark of pre-trained models such as VGG-16 (Visual Geometry Group), VGG19, Xception, ResNet152, ResNet152v2, ResNet101, ResNet101v2, DenseNet201, DenseNet169 and DenseNet121. On the benchmark datasets, viz. COVID-19 X-Ray images, an average improvement in terms of training/validation accuracy, precision, recall, and F1-scores scores were 95%, 94%, 99/88%, 99/88%, and 93/92% respectively. The results provide sufficient evidence that deep learning can be used efficiently for the detection of COVID-19 symptoms.