A Novel Medical Support Deep Learning Fusion Model for the Diagnosis of COVID-19

Goal: Medical support systems became very important for the decision-making especially in the current world medical war (Corona Virus). Methods: Lung CT images and textual information of cases collected by the Italian society of medical and interventional radiology site are used to create a textual-image fusion database consisting of 120 cases of COVID19 and other diseases. Machine Learning (ML) algorithm called Random Forests (RF) is applied to design the textual model, while the GoogleNet DL network is used to build the image model. A new fusion technique is used to get the final decision. Results: The medical support system achieves 96.7% accuracy for COVID-19 diagnosis. Conclusions: The designed medical support model can be used for the detection of COVID-19 in order to support the physician's decision especially for people who cannot pay or wait for the Polymerase Chain Reaction (PCR) test results. The model has accurate results with a maximum error of 4.5%.