To control the spread of the COVID-19 virus and to gain critical time in controlling the spread of the disease, rapid and accurate diagnostic methods based on artificial intelligence are urgently needed. In this article, we propose a clinical decision support system for the early detection of COVID 19 using deep learning based on chest radiographic images. For this we will develop an in-depth learning method which could extract the graphical characteristics of COVID-19 in order to provide a clinical diagnosis before the test of the pathogen. For this, we collected 100 images of cases of COVID-19 confirmed by pathogens, 100 images diagnosed with typical viral pneumonia and 100 images of normal cases. The architecture of the proposed model first goes through a preprocessing of the input images followed by an increase in data. Then the model begins a step to extract the characteristics followed by the learning step. Finally, the model begins a classification and prediction process with a fully connected network formed of several classifiers. Deep learning and classification were carried out using the VGG convolutional neural network. The proposed model achieved an accuracy of 92.5% in internal validation and 87.5% in external validation. For the AUC criterion we obtained a value of 97% in internal validation and 95% in external validation. Regarding the sensitivity criterion, we obtained a value of 92% in internal validation and 87% in external validation. The results obtained by our model in the test phase show that our model is very effective in detecting COVID-19 and can be offered to health communities as a precise, rapid and effective clinical decision support system in COVID-19 detection.

To combat the spread of COVID 19, the World Health Organization suggests a large-scale implementation of COVID 19 tests. Unfortunately, these tests are expensive and cannot be provided and available for people in rural and remote areas. To remedy this problem, we will develop an intelligent clinical decision support system (SADC) for the early diagnosis of COVID 19 from chest x-rays which are more accessible for people in rural areas. Thus, we collected a total of 566 radiological images classified into 3 classes: a class of COVID19 type, a Class of Pneumonia type and a class of Normal type. In the experimental analysis, 70% of the data set was used as training set and 30% was used as the test set. After preprocessing process, we use some augmentation using a rotation, a horizontal flip, a channel shift and rescale. Our finale classifier achieved the best performance with test accuracy of 99%, f1score 98%, precision of 98.60% and sensitivity 98.30%.