Computer‑aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy

Firmino Filho, José Macedo; Angelo, Giovani; Morais, Higor; Dantas, Marcel R.; Valentim, Ricardo Alexsandro de Medeiros

Computer‑aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy

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dc.contributor.author	Firmino Filho, José Macedo
dc.contributor.author	Angelo, Giovani
dc.contributor.author	Morais, Higor
dc.contributor.author	Dantas, Marcel R.
dc.contributor.author	Valentim, Ricardo Alexsandro de Medeiros
dc.date.accessioned	2020-06-24T18:32:05Z
dc.date.available	2020-06-24T18:32:05Z
dc.date.issued	2016
dc.description.resumo	Background: CADe and CADx systems for the detection and diagnosis of lung cancer have been important areas of research in recent decades. However, these areas are being worked on separately. CADe systems do not present the radiological characteristics of tumors, and CADx systems do not detect nodules and do not have good levels of automation. As a result, these systems are not yet widely used in clinical settings. Methods: The purpose of this article is to develop a new system for detection and diagnosis of pulmonary nodules on CT images, grouping them into a single system for the identification and characterization of the nodules to improve the level of automation. The article also presents as contributions: the use of Watershed and Histogram of oriented Gradients (HOG) techniques for distinguishing the possible nodules from other structures and feature extraction for pulmonary nodules, respectively. For the diagnosis, it is based on the likelihood of malignancy allowing more aid in the decision making by the radiologists. A rule-based classifier and Support Vector Machine (SVM) have been used to eliminate false positives. Results: The database used in this research consisted of 420 cases obtained randomly from LIDC-IDRI. The segmentation method achieved an accuracy of 97 % and the detection system showed a sensitivity of 94.4 % with 7.04 false positives per case. Different types of nodules (isolated, juxtapleural, juxtavascular and ground-glass) with diameters between 3 mm and 30 mm have been detected. For the diagnosis of malignancy our system presented ROC curves with areas of: 0.91 for nodules highly unlikely of being malignant, 0.80 for nodules moderately unlikely of being malignant, 0.72 for nodules with indeterminate malignancy, 0.67 for nodules moderately suspicious of being malignant and 0.83 for nodules highly suspicious of being malignant. Conclusions: From our preliminary results, we believe that our system is promising for clinical applications assisting radiologists in the detection and diagnosis of lung cancer	pt_BR
dc.identifier.citation	FIRMINO, Macedo; ANGELO, Giovani; MORAIS, Higor; DANTAS, Marcel R.; VALENTIM, Ricardo. Computer-aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy. Biomedical Engineering Online (Online), v. 15, p. 2, 2016. Disponível em: https://biomedical-engineering-online.biomedcentral.com/articles/10.1186/s12938-015-0120-7. Acesso em: 18 Jun. 2020. https://doi.org/10.1186/s12938-015-0120-7	pt_BR
dc.identifier.doi	10.1186/s12938-015-0120-7
dc.identifier.issn	1475-925X
dc.identifier.uri	https://repositorio.ufrn.br/jspui/handle/123456789/29348
dc.language	en	pt_BR
dc.publisher	Biomedical Engineering Online	pt_BR
dc.rights	Attribution 3.0 Brazil	*
dc.rights.uri	http://creativecommons.org/licenses/by/3.0/br/	*
dc.subject	Computer-aided detection system	pt_BR
dc.subject	Lung cancer diagnosis	pt_BR
dc.subject	Medical image analysis	pt_BR
dc.subject	Detection of pulmonary nodules	pt_BR
dc.subject	Likelihood of malignancy	pt_BR
dc.subject	CADe and CADx	pt_BR
dc.title	Computer‑aided detection (CADe) and diagnosis (CADx) system for lung cancer with likelihood of malignancy	pt_BR
dc.type	article	pt_BR