Thyroid Disorder Diagnosis by Optimal Convolutional Neuron based CNN Architecture

ABSTRACT The diagnosis of thyroid via appropriate interpretation of thyroid data is the vital classification issue. Only little contributions are made so far in the automatic diagnosis of thyroid disease. In order to solve Thyroid disorder this paper intends to propose a new thyroid diagnosis model, utilising two-phases includes Feature Extraction and Classification. In the first phase, two sorts of features are extracted that include image features like neighbourhood-based and gradient features, and Principal Component Analysis (PCA) is used to extract the data features as well. Subsequently, two sorts of classification processes are performed. Specifically, Convolutional Neural Network (CNN) is used for image classification by extracting deep features. Neural Network (NN) is used for classifying the disease by obtaining both the image and data features as the input. Finally, both the classified results (CNN and NN) are combined to increase the accuracy rate of diagnosis. Further, as the main aim of this work is to increase the accuracy rate, this paper aims to trigger the optimisation concept. The convolutional layer of CNN is optimally selected, and while classifying under NN the given features should be the optimal one. Hence, the required features are optimally selected. For these optimisations, a new modified algorithm is proposed in this work namely Worst Fitness-based Cuckoo Search (WF-CS) which is the modified form of Cuckoo Search Algorithm (CS). Finally, the performance of proposed WF-CS is compared over other conventional methods like Conventional CS, Genetic Algorithm (GA), FireFly (FF), Artificial Bee Colony (ABC), and Particle Swarm Optimisation (PSO) and proves the superiority of proposed work in detecting the presence of thyroid.