Radiomics for the Classification of Radiological Patterns in Computed Tomography

Revista de Informática Teórica e Aplicada 2026 Open access

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Authors: Heloísa Dias Viotto, Felipe Duarte Silva, Guilherme Eduardo Gonçalves da Silva, Heloisa de Oliveira Pinho, Ricardo Quer do Nascimento Filho, Lucas Ferrari de Oliveira
DOI: 10.22456/2175-2745.150929
Cited by: 0

Abstract

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.

Fields of Study & Subjects

Radiomics and Machine Learning in Medical Imaging Image Retrieval and Classification Techniques Artificial Intelligence in Healthcare

Publication details

Journal / source
Revista de Informática Teórica e Aplicada
Volume
33
Issue
2
Pages
145–153
Publication date
10 March 2026
Language
EN