Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/68324
Title: An adaptive feature extraction model for classification of thyroid lesions in ultrasound images
Authors: Hatwib Mugasa
Sumeet Dua
Joel E.W. Koh
Yuki Hagiwara
Oh Shu Lih
Chakri Madla
Pailin Kongmebhol
Kwan Hoong Ng
U. Rajendra Acharya
Keywords: Computer Science
Issue Date: 1-Mar-2020
Abstract: © 2020 Elsevier B.V. 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 × 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.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85079357186&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/68324
ISSN: 01678655
Appears in Collections:CMUL: Journal Articles

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