Dynamic mutation based glowworm swarm optimization with long short-term memory approaches for thyroid nodule classification

S Sathyapriya; D Anitha  lowast

doi:10.17485/IJST/v13i14.38

Article

Dynamic mutation based glowworm swarm optimization with long short-term memory approaches for thyroid nodule classification

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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v13i14.38

Year: 2020, Volume: 13, Issue: 14, Pages: 1523-1534

Original Article

Dynamic mutation based glowworm swarm optimization with long short-term memory approaches for thyroid nodule classification

S Sathyapriya¹ , D Anitha^2∗

¹ Research Scholar, Department of computer Science, Sri Ramakrishna Arts and Science College for women, 641044, Tamil Nadu, India
² Associate Professor, Department of computer science, Sri Ramakrishna Arts and Science College for women, 641044, Tamil Nadu, India

∗Corresponding author
D Anitha Associate Professor, Department of computer science, Sri Ramakrishna Arts and Science College for women, 641044, Tamil Nadu, India
[email protected]

Received Date:28 March 2020, Accepted Date:01 April 2020, Published Date:31 May 2020

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: To design an efficient approach for thyroid nodule classification with higher true positive rate. Methodology and statistical analysis: The proposed system designed as a Dynamic Mutation based Glowworm Swarm Optimization with Long-Short Term Memory (DMGSO with LSTM) scheme for thyroid nodule classification. In this proposed research work, input thyroid images are preprocessed by using Dynamically Weighted Median Filter (DWMF). The preprocessed images are segmented with the help of Region based Active Contour scheme. An Improved Local Binary Pattern (ILBP), Grey Level Cooccurrence Matrix (GLCM) and Histogram of Oriented Gradient (HOG) features are extracted from segmented image. Then the optimal features are selected by using Dynamic Mutation based Glowworm Swarm Optimization (DMGSO) algorithm. Finally, the Long-Short Term Memory (LSTM) scheme is utilized for classifying the thyroid nodule. Findings: The experimental results show that the proposed system achieves better performance compared with the existing system in terms of accuracy, precision, recall and f-measure.

Keywords: Thyroid nodule; Histogram of Oriented Gradient (HOG); Long Short-Term Memory (LSTM); Dynamic Mutation based Glow worm Swarm Optimization (DMGSO)

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Copyright

Copyright: © 2020 Sathyapriya, Anitha. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)