Automatic Detection and Classification of Lung Nodules in CT Image Using Optimized Neuro Fuzzy Classifier with Cuckoo Search Algorithm.


Journal

Journal of medical systems
ISSN: 1573-689X
Titre abrégé: J Med Syst
Pays: United States
ID NLM: 7806056

Informations de publication

Date de publication:
13 Feb 2019
Historique:
received: 07 12 2018
accepted: 21 01 2019
entrez: 14 2 2019
pubmed: 14 2 2019
medline: 14 6 2019
Statut: epublish

Résumé

The Lung nodules are very important to indicate the lung cancer, and its early detection enables timely treatment and increases the survival rate of patient. Even though lots of works are done in this area, still improvement in accuracy is required for improving the survival rate of the patient. The proposed method can classify the stages of lung cancer in addition to the detection of lung nodules. There are two parts in the proposed method, the first part is used for classifying normal/abnormal and second part is used for classifying stages of lung cancer. Totally 10 features from the lung region segmented image are considered for detection and classification. The first part of the proposed method classifies the input images with the aid of Naive Bayes classifier as normal or abnormal. The second part of the system classifies the four stages of lung cancer using Neuro Fuzzy classifier with Cuckoo Search algorithm. The results of proposed system show that the rate of accuracy of classification is improved and the results are compared with SVM, Neural Network and Neuro Fuzzy Classifiers.

Identifiants

pubmed: 30758682
doi: 10.1007/s10916-019-1177-9
pii: 10.1007/s10916-019-1177-9
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

77

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Auteurs

R Manickavasagam (R)

HOD, Department of BME, Alpha College of Engineering, Chennai, 124, India. manick6apr1979@gmail.com.

S Selvan (S)

St. Peter's College of Engineering and Technology, Avadi, Chennai, 54, India.

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Classifications MeSH