Lung Disease Classification Using Support Vector Machine
Abstract— Classification plays a vital role in disease detection and diagnosis. Classification of lung diseases is an important part for disease diagnosis. It assists diagnosis of disease with greater efficiency. Here Computed tomography (CT) images of lung diseases are classified. In this data mining classification algorithm, support vector machine (SVM) is to be optimized using ant colony optimization (ACO) algorithm. The feature of the CT scan image is extracted using wavelet transformation and the moment invariants, it is believed that it will provide a better output for classification. The Further principle component analysis also provides reduced dimensionality of image it is an added advantage for efficient classification. This optimized svm will provide a better classification accuracy.
Index Terms— Ant colony optimization (ACO), Computed tomography (CT), Principle component analysis (PCA), Support vector machine (SVM), Moment Invariant (MI).
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International Journal for Trends in Technology & Engineering © 2015 IJTET JOURNAL