NDLI: Multi-objectives optimisation of features selection for the classification of thyroid nodules in ultrasound images

Content Provider	IET Digital Library
Author	Aboudi, Noura Guetari, Ramzi Khlifa, Nawres
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.
Starting Page	1901
Ending Page	1908
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 9, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.1540
Journal	IET Image Processing
Publisher Date	2020-03-24
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	447 US Images Benign Nature Benign Nodules Biological Organs Biology And Medical Computing Biomedical Ultrasonics CAD Cancer Computer Vision And Image Processing Technique Computer-aided Diagnostic System Early-stage Thyroid Nodule Feature Extraction Feature Selection Feature Selection Method Image Classification Image Texture Knowledge Engineering Technique Leading Diagnostic Method Malignant Nature Malignant Nodules Medical Image Processing Multiobjective Particle Swarm Optimisation Algorithm Multiobjectives Optimisation Optical, Image And Video Signal Processing Optimisation Technique Particle Swarm Optimisation Patient Diagnostic Method And Instrumentation Pattern Classification Sonic And Ultrasonic Radiation Statistical Feature Extraction Method Statistics Support Vector Machine Thyroid Nodules Ultrasound Image Ultrasound Imaging
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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