Optimised hybrid classifiers for automatic HEp‐2 cell classification

Abstract

This paper introduces a new HEp-2 cell classification model under two phases. In the initial phase, the input image is segmented using the morphological operations (opening and closing), and the segmented image is given for Convolutional Neural Network (CNN) classifier that gives the classified output. In the second phase, the given input is processed under (i) segmentation process (ii) Feature Extraction, and (iii) Classification. From the segmented images; the features like Gray level co-occurrence Matrix TGLCM) and Gray level Run Length Matrix (GLRM) are extracted. After extracting the features, they are subjected to a classification process, where Neural Network (NN) is used. Finally, the mean of both classified output (first phase and second phase) is considered to be the final classified output. As the main contribution, to enhance the classification accuracy, the hidden neurons of both classifiers (CNN and NN) are optimally chosen during the classification process. To make this possible, this paper aims to propose a new Randomized Update based Grey Wolf Optimization (RP-GWO) algorithm. Finally, the performance of the implemented approach is compared over other conventional approaches and its superiority is proven with respect to certain measures.