Artificial Intelligence Based Diagnosis for Cervical Lymph Node Malignancy Using the Point-Wise Gated Boltzmann Machine

This paper aims to build an artificial intelligence (AI) architecture for automated extraction of learned-from-data image features from contrast-enhanced ultrasound (CEUS) videos and to evaluate the AI architecture for classification between benign and malignant cervical lymph nodes. An AI architecture for CEUS feature extraction and classification was constructed by using the point-wise gated Boltzmann machine (PGBM). The PGBM consisted of task-relevant and task-irrelevant hidden units for both feature learning and feature selection, and the task-relevant units were connected to the support vector machine (SVM) to yield the likelihood for classification. The synthetic minority over-sampling technique was used to improve the classification ability for an unbalanced data set. Experimental evaluation was performed with the five-fold cross validation on a database of 127 lymph nodes (39 benign and 88 malignant) from 88 patients. The SVM likelihood exhibited a significant difference between benign and malignant cervical lymph nodes (0.74±0.21 versus 0.33±0.28, p <; 0.001). On the test set, the accuracy, precision, sensitivity, specificity, and Youden's index of the AI architecture were 82.55%, 89.58%, 84.75%, 77.56%, and 62.32%, respectively. The AI architecture using the PGBM shows promising classification results, and it may be potentially used in clinical diagnosis for cervical lymph node malignancy.