An adaptive feature extraction model for classification of thyroid lesions in ultrasound images

Mugasa, Hatwib and Dua, Sumeet and Koh, Joel E. W. and Hagiwara, Yuki and Lih, Oh Shu and Madla, Chakri and Kongmebhol, Pailin and Ng, Kwan Hoong and Acharya, U. Rajendra (2020) An adaptive feature extraction model for classification of thyroid lesions in ultrasound images. Pattern Recognition Letters, 131. pp. 463-473. ISSN 0167-8655, DOI

Full text not available from this repository.


The thyroid is the chief hormonal gland that controls the growth, metabolism, and maturation of the body. However, the function of the thyroid gland could be disrupted if it produces too much or too little hormones. Furthermore, there could be abnormal growth in thyroid cell tissue, leading to the formation of a benign or malignant thyroid lesion. Ultrasound is a typical non-invasive diagnosis approach to check for cancerous thyroid lesions. However, the visual interpretation of the ultrasound thyroid images is challenging and time-consuming. Hence, a feature engineering model is proposed to overcome these challenges. We propose to transform image pixel intensity values into high dimensional structured data set before fitting a Regression analysis framework to estimate kernel parameters for an image filter model. We then adopt a Bayesian network inference to estimate a subset for the textural features with a significant conditional dependency in the classification of thyroid lesions. The analysis of the proposed feature engineering model showed that the classification performance had an overall significant improvement over other image filter models. We achieve 96.00% classification accuracy with a sensitivity and specificity of 99.64% and 90.23% respectively for a filter size of 13 x 13. The analysis of results indicate that the diagnosis of ultrasound images thyroid nodules is significantly boosts by adaptively learning filter parameters for feature engineering model. (C) 2020 Elsevier B.V. All rights reserved.

Item Type: Article
Uncontrolled Keywords: Feature extraction; Image filtering; Benign and malignant thyroid lesion; Image texture feature; Computer-aided diagnosis system; Ultrasound
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
R Medicine
Divisions: Faculty of Medicine
Depositing User: Ms Zaharah Ramly
Date Deposited: 05 Oct 2023 04:13
Last Modified: 05 Oct 2023 04:13

Actions (login required)

View Item View Item