title: Computer-aided diagnosis system for lung nodules based on computed tomography using shape analysis, a genetic algorithm, and SVM.
abstract: Lung cancer is the major cause of death among patients with cancer worldwide. This work is intended to develop a methodology for the diagnosis of lung nodules using images from the Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI). The proposed methodology uses image processing and pattern recognition techniques. To differentiate the patterns of malignant and benign forms, we used a Minkowski functional, distance measures, representation of the vector of points measures, triangulation measures, and Feret diameters. Finally, we applied a genetic algorithm to select the best model and a support vector machine for classification. In the test stage, we applied the proposed methodology to 1405 (394 malignant and 1011 benign) nodules from the LIDC-IDRI database. The proposed methodology shows promising results for diagnosis of malignant and benign forms, achieving accuracy of 93.19 %, sensitivity of 92.75 %, and specificity of 93.33 %. The results are promising and demonstrate a good rate of correct detections using the shape features. Because early detection allows faster therapeutic intervention, and thus a more favorable prognosis for the patient, herein we propose a methodology that contributes to the area.
authors.affiliation: Applied Computing Group - NCA, Federal University of Maranhão - UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, São Luís, MA, 65085-580, Brazil. antoniooseas@gmail.com.
authors.forename: Antonio Oseas
authors.initials: AO
authors.lastname: de Carvalho Filho
authors.affiliation: Applied Computing Group - NCA, Federal University of Maranhão - UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, São Luís, MA, 65085-580, Brazil.
authors.forename: Aristófanes Corrêa
authors.initials: AC
authors.lastname: Silva
authors.affiliation: Applied Computing Group - NCA, Federal University of Maranhão - UFMA, Av. dos Portugueses, SN, Campus do Bacanga, Bacanga, São Luís, MA, 65085-580, Brazil.
authors.forename: Anselmo Cardoso
authors.initials: AC
authors.lastname: de Paiva
authors.affiliation: State University of Rio de Janeiro, Sao Francisco de Xavier, 524, Maracana, Rio de Janeiro, RJ, 20550-900, Brazil.
authors.forename: Rodolfo Acatauassú
authors.initials: RA
authors.lastname: Nunes
authors.affiliation: Department of Computer Science, Pontifical Catholic University of Rio de Janeiro - PUC-Rio, R. Marquês de São Vicente, 225, Gávea, Rio de Janeiro, RJ, 22453-900, Brazil.
authors.forename: Marcelo
authors.initials: M
authors.lastname: Gattass
mesh_terms: D000465:Algorithms; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008957:Models, Genetic; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D003074:Solitary Pulmonary Nodule / Q000000981:diagnostic imaging* / Q000473:pathology*; D060388:Support Vector Machine; D014057:Tomography, X-Ray Computed / Q000379:methods*
keywords: Genetic algorithm; Lung cancer; Medical image; Shape analysis