Screening of COVID-19 Suspected Subjects Using Multi-Crossover Genetic Algorithm Based Dense Convolutional Neural Network

Abstract

Fast and accurate screening of novel coronavirus (COVID-19) suspected subjects plays a vital role in timely quarantine and medical care. Deep transfer learning-based screening models on chest X-ray (CXR) are effective for countering the COVID-19 outbreak. However, an efficient screening of COVID-19 is still a huge task due to the spatial complexity of CXRs. In this paper, a dense convolutional neural network (DCov-Net) based transfer learning model is proposed for the screening of COVID-19 suspected subjects using CXR images. A modified multi-crossover genetic algorithm (MMCGA) is then proposed to tune the hyper-parameters of DCov-Net. Majority of the existing COVID-19 diagnosis models are not interpretable as they do not provide any transparency to the users. Therefore, the concept of heat-maps is used to achieve explainability and interpretability. MMCGA based DCov-Net is implemented on a multiclass dataset that contains four different classes. Experimental results reveal that MMCGA based DCov-Net achieves better performance than the existing models. The proposed MMCGA based DCov-Net can be utilized for initial screening of COVID-19 suspected subjects with an accuracy of 99.34 +/- 0.51 %.