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

07:00 EST 13th February 2019 | BioPortfolio

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

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.

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Journal Details

This article was published in the following journal.

Name: Journal of medical systems
ISSN: 1573-689X
Pages: 77

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A number of small lung lesions characterized by small round masses of 2- to 3-mm in diameter. They are usually detected by chest CT scans (COMPUTED TOMOGRAPHY, X-RAY). Such nodules can be associated with metastases of malignancies inside or outside the lung, benign granulomas, or other lesions.

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