Lung cancer prediction and retrieval using multistage hybrid prediction approach
Keywords:
lung nodule, retrieval, prediction, feature extraction, classifierAbstract
In cancer diagnosis computer-aided Prediction is considered as significant research domain. The generation and processing of lung cancer identification and Prediction is expensive. During past decades, content-based image retrieval (CBIR) technique has been applied in various medical applications. For effective diagnosis of lung cancer radiologist’s requires effective approach for cancer prediction and diagnosis. In this research, for prediction of CT lung images is achieved using hybrid Prediction approach is adopted with integration of logistic regression and Adaboost classifier. Feature extraction and retrieval of lung cancer image applied through optimization technique known as firefly approach for processing several vector features of lung images to derive salient characteristics for achieving best features. Results illustrated that proposed prediction and retrieval approach exhibits significant performance rather than conventional technique. The proposed approach provides classification accuracy of 97% which is significantly higher.
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