Ultrasound-Based Machine Learning-Aided Detection of Uterine Fibroids: Integrating Vision Transformer for Improved Analysis

Abstract

The primary objective of this study is to segment the uterine fibroids (leiomyoma) from the ultrasound images of the uterus through semantic segmentation, followed by second-order statistical feature extraction using the Gray-level Co-occurrence Matrix (GLCM). The next objective of the study is to compare the performance of the state-of-the-art method namely Vision Transformer (ViT) with three different machine learning (ML) classifiers such as the Support Vector Machine (SVM), Logistic Regression (LR) and [Formula: see text]-Nearest Neighbor ([Formula: see text]-NN) to classify the images into uterine fibroid and normal. The dataset consists of 50 ultrasound images of uterine fibroids and 50 normal images. Then the images are segmented using region-growing-based semantic segmentation followed by feature extraction and classification using the ML and deep learning (DL) classifiers. Among the ML classifiers, SVM produced a good accuracy of 93.1% compared to the other classifiers. ViT produced an excellent classification accuracy of 97.5%. Hence, ViT outperformed compared to the ML classifiers in uterine fibroid detection. These findings have important implications for clinical practice, as they could help physicians to diagnose and treat uterine fibroids more effectively.