A Weighted Bag of Visual Words Model for Predicting Fetal Growth Restriction at an Early Stage

Ani Dong¹Yiheng Zhang²Weiling Li^2*

• ¹Dongguan City University, Dongguan, China
• ²Dongguan University of Technology, Dongguan, China

Fetal growth restriction (FGR) is a significant concern for clinicians and pregnant women, as it is associated with increased fetal and neonatal mortality and morbidity. Although ultrasound has been the gold standard for many years to define FGR, it remains less than ideal for early detection of FGR. Placental dysfunction is a key factor in the development of FGR. The objective of this study is to achieve the early detection of FGR through the utilization of placental ultrasound images.Methods: Retrospective analysis collected 80 placental ultrasound images from 40 FGR fetuses and 40 normal fetuses of appropriate gestational age. Approximately 300 texture features were extracted from placental images using key texture feature selection and Histo-grams of Oriented Gradients (HOG) feature extraction methods. Then, these features were reencoded using bag of visual words model based on weight scaling, resulting in more effective features. The encoded image features can be trained using a classifier, and finally ensemble prediction can be used to improve classification accuracy.In this article, we applied the proposed method and popular image classification methods to FGR prediction. The proposed method achieved the best experimental results, with an accuracy of 70% and an F1 Score of 0.7653. We also compared different feature extraction methods separately, and the experimental results showed that HOG feature extraction is more suitable for feature extraction of ultrasound placental images. Finally, we plotted the ROC curve with an AUC value of 0.80.In order to predict FGR at an early stage, we propose a visual bag-of-words model based on weight scaling for analyzing placental ultrasound images in the early stages before fetal impaired.The proposed model shows its potential to assist doctors in making preliminary judgments, which greatly helps with treatment and allows patients to receive treatment as soon as possible, reducing the harm to fetuses and pregnant women.