Feature selection and classification using bio‐inspired algorithms for the diagnosis of pulmonary emphysema subtypes

Abstract

A computer-assisted diagnosis framework to examine computed tomography (CT) slices for diagnosing pulmonary emphysema is designed. Partitioning of lung tissues and regions of Interest (ROIs) from the CT slices is achieved using Spatial Intuitionistic Fuzzy C-Means (SIFCM) clustering algorithm. Shape features, texture features, and run-length features are extracted from each ROI. Feature selection is performed as a wrapper technique by employing manta ray foraging optimization (MRFO) algorithm and random forest (RF) classifier. A backpropagation neural network (BPNN) using gradient descent is used to train the selected features. Adaptive butterfly optimization algorithm (ABOA) is used to fix the optimal topology and parameters, namely weights and biases of the neural network. The BPNN classifier is initialized with the optimized topology and parameters obtained by the ABOA. The developed framework attained an accuracy of 87.52% when tested on an emphysema dataset, which outperforms the BPNN classifier in terms of accuracy, specificity, precision, and recall.