Classification of Thyroid Nodules Using Follow the Regularized Leader Optimization Based Deep Neural Networks

Abstract

Thyroid cancer, the most common endocrine malignancy, has exhibited a steadily increasing incidence worldwide. Recently, ultrasound (US) imaging has been recognized as a potential diagnostic tool for early assessment of thyroid nodules. However, visual interpretation of nodules is subject to the radiologists' subjective evaluations. To address this, a computer-aided Diagnostic (CAD) system has been developed to differentiate between benign and malignant thyroid nodules. The efficiency of this nodule classifier is heavily dependent on the features employed in the classification process. In this study, the efficacy of the RREMI-RF approach, employing the Multi-Channel Convolutional Neural Network (MCNN) and Hybrid Feature Cropping Network (HFCN) techniques, was evaluated. An innovative Follow the Regularised Leader-based Deep Neural Network (FTRL-DNN) technique is proposed for the precise classification of thyroid nodules. In this method, the AlexNet learning-based feature extraction system was utilized to extract features during the classification process. Images from the Digital Database Thyroid Image (DDTI) dataset were classified using the Long Short Term Memory (LSTM) classifier. Performance metrics, including accuracy, sensitivity, specificity, precision, and error rate, were used to assess the effectiveness of the FTRL-DNN algorithm. Compared to the HFCN and MCNN, the FTRL-DNN-based thyroid nodule classification demonstrated superior accuracy, achieving a rate of 98.94%. This research presents a significant advancement in the automated classification of thyroid nodules, potentially improving early detection and treatment of thyroid cancer.