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.

摘要

通过对甲状腺数据的适当解释来诊断甲状腺是至关重要的分类问题。迄今为止，在甲状腺疾病的自动诊断方面做出的贡献很少。为了解决甲状腺疾病，本文拟提出一种新的甲状腺诊断模型，利用特征提取和分类两个阶段。在第一阶段，提取两种特征，包括基于邻域和梯度特征等图像特征，并使用主成分分析（PCA）来提取数据特征。随后，执行两种分类处理。具体来说，卷积神经网络 (CNN) 通过提取深度特征用于图像分类。神经网络 (NN) 用于通过获取图像和数据特征作为输入来对疾病进行分类。最后，将两个分类结果（CNN和NN）结合起来，以提高诊断的准确率。此外，由于这项工作的主要目的是提高准确率，本文旨在触发优化概念。CNN 的卷积层是最优选择的，在 NN 下进行分类时，给定的特征应该是最优的。因此，所需的特征被最佳地选择。对于这些优化，在这项工作中提出了一种新的改进算法，即基于最差适应度的布谷鸟搜索 (WF-CS)，它是布谷鸟搜索算法 (CS) 的改进形式。最后，将提出的 WF-CS 的性能与其他传统方法进行比较，如传统 CS、遗传算法 (GA)、