Energy-efficient model “DenseNet201 based on deep convolutional neural network” using cloud platform for detection of COVID-19 infected patients

Abstract Objective The outbreak of the coronavirus caused major problems in more than 151 countries around the world. An important step in the fight against coronavirus is the search for infected people. The goal of this article is to predict COVID-19 infectious patients. Methods We implemented DenseNet201, available on cloud platform, as a learning network. DenseNet201 is a 201-layer networkthat. is trained on ImageNet. The input size of pre-trained DenseNet201 images is 224 × 224 × 3. Results Implementation of DenseNet201 was effectively performed based on 80 % of the training X-rays and 20 % of the X-rays of the test phases, respectively. DenseNet201 shows a good experimental result with an accuracy of 99.24 % in 7.47 min. To measure the computational efficiency of the proposed model, we collected more than 6,000 noise-free data infected by tuberculosis, COVID-19, and uninfected healthy chests for implementation. Conclusions DenseNet201 available on the cloud platform has been used for the classification of COVID-19-infected patients. The goal of this article is to demonstrate how to achieve faster results.