Since the beginning of the COVID-19 outbreak, the scientific communityhas been making efforts in several areas, either by seekingvaccines or improving the early diagnosis of the disease to contributeto the fight against the SARS-CoV-2 virus. The use of X-rayimaging exams becomes an ally in early diagnosis and has been thesubject of research by the medical image processing and analysiscommunity. Although the diagnosis of diseases by image is a consolidatedresearch theme, the proposed approach aims to: a) applystate-of-the-art machine learning techniques in X-ray images forthe COVID-19 diagnosis; b) identify COVID-19 features in imagingexamination; c) to develop an Artificial Intelligence model toreduce the disease diagnosis time; in addition to demonstrating thepotential of the Artificial Intelligence area as an incentive for theformation of critical mass and encouraging research in machinelearning and processing and analysis of medical images in the Stateof Mato Grosso, in Brazil. Initial results were obtained from experimentscarried out with the SVM (Support Vector Machine) classifier,induced on a publicly available image dataset from Kaggle repository.Six attributes suggested by Haralick, calculated on the graylevel co-occurrence matrix, were used to represent the images. Theprediction model was able to achieve 82.5% accuracy in recognizingthe disease. The next stage of the studies includes the study of deeplearning models.
THE INTERPRETATION OF X-RAY PICTURES AS AN AID TO THE EARLY DIAGNOSIS OF THORACIC ANEURYSM