Lung Nodule Detection Using Hybrid Classifier
R Poornima
PG Scholar, Communication Systems, Arunai college of Engineering, Tiruvannamalai, Tamilnadu
V Sivasankaran
Assistant Professor, Department of ECE, Arunai college of Engineering, Tiruvannamalai, Tamilnadu
Receiving Date:
2016-06-18
Acceptance Date:
2016-07-29
Publication Date:
2016-09-04
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Abstract
An abnormality in lung nodule leads to lung cancer which demands an early detection of lung nodule. The study reveals that automatic detection technique of lung nodule with convincing results and increases the speed of analysis. Since the nodules are attached to blood vessels, detection of lung nodule is a challenging task. To deal with this issue MR8 (Maximum response 8) filter bank based approach is used before preprocessing and eight maximum responses are obtained. From that response texture, intensity and gradient features are extracted using LBP (Local Binary Pattern), HOG (Histogram of Oriented Gradient), SIFT (scale invariant feature transform) descriptor respectively. Further the performances of features are analyzed by hybrid classifier. Hybrid classifier approach is to embed SVM (Support Vector Machine) with ID3 (Iterative Dichotomiser).
Keywords:
lung nodule; feature extraction; SVM+ID3 classifier
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