Automated diagnosis of pulmonary emphysema using multi-objective binary thresholding and hybrid classification

Abstract

This paper is to estimate the potential of a deep learning method for automatic diagnosis of pulmonary emphysema. In the initial step, the dataset acquisition is performed by gathering a set of real-time dataset and the publicly available benchmark datasets known as the Computed Tomography Emphysema Database. After pre-processing of images, the lung segmentation is performed by the optimized binary thresholding. Here, the improvement of the segmentation is accomplished by the adoption of a hybrid meta-heuristic algorithm with Barnacles Mating Optimization (BMO), and Butterfly Optimization Algorithm (BOA) called Barnacles Mating-based Butterfly Optimization Algorithm (BM-BOA), in such a way to attain the multi-objective function concerning the variance and entropy of the image. Further, the feature descriptor called Weber Local Binary Pattern (WLBP) is used for generating the pattern image and the feature vectors. Two types of machine learning algorithms are used for the classification, in which Neural Network (NN) considers the feature vector from WLBP as input, and the deep learning model called Convolutional Neural Network (CNN) considers the WLBP pattern of the segmented image as input. In the hybrid classification model, the activation function is optimized by the same BM-BOA, which results in classifying the normal lung, mild emphysema, moderate (medium) emphysema, and severe emphysema. According to the experimental results with the comparison over the state-of-art-techniques, the proposed system permits inexpensive and reliable identification of emphysema on digital chest radiography.