RESUMO
COVID-19 is most commonly diagnosed using a testing kit but chest X-rays and computed tomography (CT) scan images have a potential role in COVID-19 diagnosis. Currently, CT diagnosis systems based on Artificial intelligence (AI) models have been used in some countries. Previous research studies used complex neural networks, which led to difficulty in network training and high computation rates. Hence, in this study, we developed the 6-layer Deep Neural Network (DNN) model for COVID-19 diagnosis based on CT scan images. The proposed DNN model is generated to improve accurate diagnostics for classifying sick and healthy persons. Also, other classification models, such as decision trees, random forests and standard neural networks, have been investigated. One of the main contributions of this study is the use of the global feature extractor operator for feature extraction from the images. Furthermore, the 10-fold cross-validation technique is utilized for partitioning the data into training, testing and validation. During the DNN training, the model is generated without dropping out of neurons in the layers. The experimental results of the lightweight DNN model demonstrated that this model has the best accuracy of 96.71% compared to the previous classification models for COVID-19 diagnosis.