Title: Machine learning-based CAD system for thyroid tumour characterisation using ultrasound images

Authors: Niranjan Yadav; Rajeshwar Dass; Jitendra Virmani

Addresses: Department of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology Murthal, Sonepat, 131039, India ' Department of Electronics and Communication Engineering, Deenbandhu Chhotu Ram University of Science and Technology Murthal, Sonepat, 131039, India ' Central Scientific Instruments Organization, Council of Scientific and Industrial Research, Chandigarh, 160030, India

Abstract: The main objective of this paper is to develop an efficient computer-aided diagnosis (CADx) system for the characterisation of thyroid tumours using US images. An efficient CADx system is designed to address speckle noise's effect on thyroid tumour classification in this work. The analysis has been carried out with 820 thyroid ultrasound images. The CADx system designs were examined based on original and despeckled images to compare the texture and morphological features. The extracted features have been used to design five CADx systems, namely: 1) PCA-KNN; 2) PCA-PNN; 3) PCA-SVM; 4) SFS-SVM; 5) LS-ANFC algorithms. The results illustrate that a CADx system based on the PCA-SVM algorithm with (LBP + ZRM) features yields optimal performance for the characterisation of thyroid tumour ultrasound images.

Keywords: thyroid ultrasound images; local binary pattern; LBP; Zernike features; support vector machine; sequential feature selection; SFS.

DOI: 10.1504/IJMEI.2024.141790

International Journal of Medical Engineering and Informatics, 2024 Vol.16 No.6, pp.547 - 559

Received: 13 Dec 2021
Accepted: 06 Jun 2022
Published online: 02 Oct 2024 *

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