Classification of Malignancy in Suspicious Lesions Using Autofluorescence Bronchoscopy

Abstract

This paper presents a novel approach to the classification of bronchial tissue as either malignant or precancerous based on autofluorescence bronchoscopy (AFB) images. The study consisted of 44 images, of which 22 were confirmed as malignant and 22 as nonmalignant precancerous cases. Our approach starts with the detection of a region of interest (ROI). This is followed by an analysis of semi-normal intensity distributions in gray-scale images of red and green components of the previously identified ROI. Based on the results of this analysis, features are computed, which are then used to build an image-classification model. This model classifies the tissue images into malignant/nonmalignant classes. We utilized several classification algorithms, i.e., naive Bayes, K-nearest-neighbor (K-NN), and support vector machine (SVM) with dot kernel. The criteria used when testing their performance were accuracy, sensitivity, specificity, and the area under the curve. Wilcoxon’s signed-rank test was used to confirm the accuracy of the classification method. The proposed method was compared to a similar approach reported by Buountris et al., who analyzed the texture features in a gray-level co-occurrence matrix (GLCM). Using the bestperforming classification algorithm (SVM with dot kernel), the accuracy of the proposed approach (95.8 %) was better than that reported by Bountris et al. (92.1 %).