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Computer-aided diagnosis (CAD) plays a vital role in the routine clinical activity for the detection of lung disorders using computed tomography (CT) images. It serves as a source of second opinion that radiologists may consider in order to interpret CT images. In this work, the purpose of CAD is to improve the diagnostic accuracy of pulmonary bronchitis from CT images of the lung.

Left and right lung fields are segmented using optimal thresholding from the lung CT images. Texture and shape features are extracted from the pathology bearing regions. A hybrid feature selection approach based on ant colony optimization (ACO) combining cosine similarity and support vector machine (SVM) classifier is used to select relevant features. Additionally, tandem run recruitment strategy is included in the selection activity to choose the promising features. The SVM classifier is trained using …