Colour-based segmentation using FCM and K-means clustering for 3D thyroid gland state image classification using deep convolutional neural network structure

Abstract

Thyroid cancer is a rare disease that often goes undetected until it reaches advanced stages. This study presents a novel approach for identifying Thyroid Imaging Reporting and Data System (TI-RADS) stages of thyroid nodules using a deep convolutional neural network (Deep-CNN) model. Our method incorporates pre-processing, segmentation and feature extraction using colour based fuzzy-c-means and K-means clustering, followed by deep learning classification. We demonstrate the efficacy of our proposed approach by achieving a success rate of 87% for classifying TI-RADS1, TI-RADS2, TI-RADS3, TI-RADS4 and TI-RADS5 stages, outperforming other state-of-the-art models. Our study fills an important gap in the literature, as previous studies have mainly focused on the margins of thyroid nodules rather than their calcium flecks. Our results offer promise for improving the accuracy and efficiency of thyroid cancer detection and management.