extended vgg16 deep-learning detects covid-19 from chest ct images

Coronavirus disease 2019 (covid-19), is a rapidly spreading disease that has infected millions of people worldwide. one of the essential steps to prevent spreading covid-19 is an effective screening of infected individuals. in addition to clinical tests like reverse transcription-polymerase chain reaction (rt-pcr), medical imaging techniques such as computed tomography (ct) can be used as a rapid technique to detect and evaluate patients infected by covid-19. conventionally, ct-based covid-19 detection is performed by an expert radiologist. in this paper, we will completely and utterly discuss covid-19. we present a deep learning convolutional neural network (cnn) model that we have developed to detect chest ct images with covid-19 lesions. afterwards, based on the fact that in an infected individual, more than one slice is involved, we determine and apply the best threshold to detect covid-19 positive patients. we collected 5,225 ct images from 130 covid-19 positive patients and 4,955 ct images from 130 healthy subjects. we used 3,684 ct images with covid-19 lesions and their corresponding slices from healthy control subjects to build our model. we used 5-fold-cross-validation to evaluate the model, in which each fold contains 26 patients and 26 healthy subjects. we obtained a sensitivity of 91.5%±6.8%, a specificity of 94.6%±3.4%, an accuracy of 93.0%±3.9%, a precision of 94.5%±3.5%, and an f1-score of 0.93±0.04.