Hybrid classification with meta‐heuristic‐enabled optimal feature selection for thyroid detection

Abstract

Thyroid is a widespread disease, affecting most victims. The diagnosis of thyroid remains a complex process, as its detection in patients is highly intricate. Hence, the doctors are needed to be aware of the risk factors and symptoms of the disease. This paper aims to propose a novel thyroid diagnosis scheme, involving three major phases: (a) feature extraction, (b) optimal feature selection, and (c) classification. Initially, the thyroid image and the related data serve as input for diagnosing the disease. In the first phase, the features like, gray level cooccurrence matrix (GLCM), gray level run length matrix (GLRM), local binary pattern (LBP), local vector pattern (LVP), and local tetra patterns (LTrP) are extracted from the input image. Additionally, the features from data are extracted using Principal Component Analysis (PCA) for resolving the issue of "curse of dimensionality." The optimal features are then selected using a hybrid