Original scientific paper
https://doi.org/10.1080/00051144.2024.2316503
Residual U-Net approach for thyroid nodule detection and classification from thyroid ultrasound images
Sheeja Agustin
; Department of Computer Science & Engineering, Marian Engineering College, Thiruvananthapuram, India
*
S. Sruthy
; The Department of Computer Science and Engineering, St. Joseph’s College of Engineering and Technology, Palai, India
Ajay James
; Department of Computer Science and Engineering, Government Engineering College, Thrissur, India
Philomina Simon
; Department of Computer Science, University of Kerala, Thiruvananthapuram, India
* Corresponding author.
Abstract
With so many thyroid knobs (nodules) discovered by accident, it is critical to recognize as many
aberrant knobs (nodules) as possible from fine-needle aspiration (FNA) biopsies or other medical
procedures while excluding those that are virtually certainly benign. Thyroid ultrasonography,
on the other hand, is prone to interobserver variability and subjective translations. An effective
deep learning model for segmenting and categorizing thyroid nodules in this study follows the
stages below: data collection from a well-known archive, The Thyroid Digital Image Database
(TDID), which comprises ultrasound pictures from 298 patients, preprocessing using anisotropic
diffusion filter (ADF) for removing noise and enhancing the images, segmentation using a bilateral filter for segmenting images, feature extraction using grey level occurrence matrix (GLCM),
feature selection using Multi-objective Particle Swarm with Random Forest Optimization (MbPSRA) and finally classification happens were Residual U-Net will be used. Experiment evaluation
states the proposed model outperforms well than other state-of-art models.
Keywords
Classification; deep learning; residual U-Net; segmentation; thyroid nodule; ultrasound images
Hrčak ID:
326082
URI
Publication date:
26.2.2024.
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