Classification of Benign-Malignant Thyroid Nodules Based on Hyperspectral Technology

• Published in: Sensors (Basel, Switzerland) Vol. 24; no. 10; p. 3197
• Main Authors: Wang, Junjie, Du, Jian, Tao, Chenglong, Qi, Meijie, Yan, Jiayue, Hu, Bingliang, Zhang, Zhoufeng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 17.05.2024
• Subjects: Accuracy; Algorithms; Artificial intelligence; Classification; Deep learning; Humans; hyperspectral image; Hyperspectral Imaging - methods; Image Processing, Computer-Assisted - methods; Machine learning; Medical diagnosis; Neural networks; Neural Networks, Computer; Remote sensing; spectral characteristics; Support vector machines; Surgery; Thyroid cancer; Thyroid Neoplasms - classification; Thyroid Neoplasms - diagnosis; Thyroid Neoplasms - pathology; Thyroid Nodule - classification; Thyroid Nodule - diagnosis; Thyroid Nodule - pathology; thyroid nodules; Ultrasonic imaging; Vital signs; Wavelet transforms; hyperspectral image; classification; thyroid nodules; spectral characteristics
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s24103197

In recent years, the incidence of thyroid cancer has rapidly increased. To address the issue of the inefficient diagnosis of thyroid cancer during surgery, we propose a rapid method for the diagnosis of benign and malignant thyroid nodules based on hyperspectral technology. Firstly, using our self-developed thyroid nodule hyperspectral acquisition system, data for a large number of diverse thyroid nodule samples were obtained, providing a foundation for subsequent diagnosis. Secondly, to better meet clinical practical needs, we address the current situation of medical hyperspectral image classification research being mainly focused on pixel-based region segmentation, by proposing a method for nodule classification as benign or malignant based on thyroid nodule hyperspectral data blocks. Using 3D CNN and VGG16 networks as a basis, we designed a neural network algorithm (V3Dnet) for classification based on three-dimensional hyperspectral data blocks. In the case of a dataset with a block size of 50 × 50 × 196, the classification accuracy for benign and malignant samples reaches 84.63%. We also investigated the impact of data block size on the classification performance and constructed a classification model that includes thyroid nodule sample acquisition, hyperspectral data preprocessing, and an algorithm for thyroid nodule classification as benign and malignant based on hyperspectral data blocks. The proposed model for thyroid nodule classification is expected to be applied in thyroid surgery, thereby improving surgical accuracy and providing strong support for scientific research in related fields.