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

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)