Multi‐objectives optimisation of features selection for the classification of thyroid nodules in ultrasound images. Issue 9 (8th June 2020)

• Record Type: Journal Article
• Authors: Aboudi, Noura; Guetari, Ramzi; Khlifa, Nawres
• Is Part Of: IET image processing. Volume 14:Issue 9(2020)
• Page Start: 1901
• Page End: 1908
• Publication Date: 2020-06-08
• Subjects: CAD -- biomedical ultrasonics -- feature extraction -- pattern classification -- image classification -- support vector machines -- cancer -- medical image processing -- biological organs -- image texture -- particle swarm optimisation; multiobjectives optimisation -- features selection -- thyroid nodules -- ultrasound images -- ultrasound imaging -- leading diagnostic method -- early‐stage thyroid nodule -- computer‐aided diagnostic systems -- benign nature -- malignant nature -- 447 US images -- statistical features extraction methods -- feature selection method -- multiobjective particle swarm optimisation algorithm -- benign nodules -- malignant nodules; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2019.1540
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Ultrasound (US) imaging is the leading diagnostic method for assessing the early‐stage thyroid nodule. However, the visual evaluation of nodules can be influenced by the subjectivity of radiologists' interpretations. Computer‐aided Diagnostic (CAD) systems can be useful in classifying these nodules according to their benign or malignant nature. The extraction of the characteristics, which relate in the author's case to the US of thyroid nodules, is essential in the differentiation of these nodules. The complex nature of images, however, generates a significant number of features, many of which are either redundant or irrelevant. This study presents a new CAD system that has been developed to categorise thyroid nodules. In this survey, 447 US images of thyroid nodules were retained. These images were used to extract features using statistical features extraction methods. A feature selection method based on the multi objective particle swarm optimisation algorithm was used to choose the most relevant and non‐redundant ones. Then, support vector machine (SVM) and random forests (RFs) were applied to classify these nodules. 10‐fold cross‐validation was used to assess the classification performance metrics. Their proposed CAD has reached a maximum accuracy of 94.28% for SVM; and 96.13% for RF using the contour‐based ROI.