Automatic Detection and Classification of Lung Nodules in CT Image Using Optimized Neuro Fuzzy Classifier with Cuckoo Search Algorithm.
Cuckoo search
Lung nodules
Naïve Bayes classifier
Neuro fuzzy classifier
Segmentation
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
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
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