A Novel N -Gram-Based Image Classification Model and Its Applications in Diagnosing Thyroid Nodule and Retinal OCT Images

Abstract

Imbalanced classes and dimensional disasters are critical challenges in medical image classification. As a classical machine learning model, the $n$ -gram model has shown excellent performance in addressing this issue in text classification. In this study, we proposed an algorithm to classify medical images by extracting their $n$ -gram semantic features. This algorithm first converts an image classification problem to a text classification problem by building an $n$ -gram corpus for an image. After that, the algorithm was based on the $n$ -gram model to classify images. The algorithm was evaluated by two independent public datasets. The first experiment is to diagnose benign and malignant thyroid nodules. The best area under the curve (AUC) is 0.989. The second experiment is to diagnose the type of fundus lesion. The best result is that it correctly identified 86.667% of patients with dry age-related macular degeneration (AMD), 93.333% of patients with diabetic macular edema (DME), and 93.333% of normal individuals.