Deep Learning and Handcrafted Features for Thyroid Nodule Classification

Abstract

In this research, we present a refined image-based computer-aided diagnosis (CAD) system for thyroid cancer detection using ultrasound imagery. This system integrates a specialized convolutional neural network (CNN) architecture designed to address the unique aspects of thyroid image datasets. Additionally, it incorporates a novel statistical model that utilizes a two-dimensional random coefficient autoregressive (2D-RCA) method to precisely analyze the textural characteristics of thyroid images, thereby capturing essential texture-related information. The classification framework relies on a composite feature vector that combines deep learning features from the CNN and handcrafted features from the 2D-RCA model, processed through a support vector machine (SVM) algorithm. Our evaluation methodology is structured in three phases: initial assessment of the 2D-RCA features, analysis of the CNN-derived features, and a final evaluation of their combined effect on classification performance. Comparative analyses with well-known networks such as VGG16, VGG19, ResNet50, and AlexNet highlight the superior performance of our approach. The outcomes indicate a significant enhancement in diagnostic accuracy, achieving a classification accuracy of 97.2%, a sensitivity of 84.42%, and a specificity of 95.23%. These results not only demonstrate a notable advancement in the classification of thyroid nodules but also establish a new standard in the efficiency of CAD systems.