Radiomics for the Classification of Radiological Patterns in Computed Tomography

Pulmonary diseases are a major issue today, being one of the leading causes of death worldwide. Because of this, it is essential to develop methods to improve their diagnosis. This study arises from this problem, with the aim of identifying radiomic features in Computed Tomography (CT) and to evaluate the performance of different machine learning algorithms, including KNN, SVM, RF, and MLP. The results obtained show satisfactory values, with specificity standing out. SVM had the best performance with, with a mean specificity of 96.25% and a mean sensitivity of 85.05% both with a small standard deviation.