Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers

Author:

Ghabri Haifa,Alqahtani Mohammed S.,Ben Othman Soufiene,Al-Rasheed Amal,Abbas Mohamed,Almubarak Hassan Ali,Sakli Hedi,Abdelkarim Mohamed Naceur

Abstract

AbstractUltrasound imaging is commonly used to aid in fetal development. It has the advantage of being real-time, low-cost, non-invasive, and easy to use. However, fetal organ detection is a challenging task for obstetricians, it depends on several factors, such as the position of the fetus, the habitus of the mother, and the imaging technique. In addition, image interpretation must be performed by a trained healthcare professional who can take into account all relevant clinical factors. Artificial intelligence is playing an increasingly important role in medical imaging and can help solve many of the challenges associated with fetal organ classification. In this paper, we propose a deep-learning model for automating fetal organ classification from ultrasound images. We trained and tested the model on a dataset of fetal ultrasound images, including two datasets from different regions, and recorded them with different machines to ensure the effective detection of fetal organs. We performed a training process on a labeled dataset with annotations for fetal organs such as the brain, abdomen, femur, and thorax, as well as the maternal cervical part. The model was trained to detect these organs from fetal ultrasound images using a deep convolutional neural network architecture. Following the training process, the model, DenseNet169, was assessed on a separate test dataset. The results were promising, with an accuracy of 99.84%, which is an impressive result. The F1 score was 99.84% and the AUC was 98.95%. Our study showed that the proposed model outperformed traditional methods that relied on the manual interpretation of ultrasound images by experienced clinicians. In addition, it also outperformed other deep learning-based methods that used different network architectures and training strategies. This study may contribute to the development of more accessible and effective maternal health services around the world and improve the health status of mothers and their newborns worldwide.

Publisher

Springer Science and Business Media LLC

Subject

Multidisciplinary

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Ensemble learning for fetal ultrasound and maternal–fetal data to predict mode of delivery after labor induction;Scientific Reports;2024-07-03

2. Enhancing Fetal Classification Accuracy through Computer-Aided Impainting Techniques;2024 IEEE 12th International Symposium on Signal, Image, Video and Communications (ISIVC);2024-05-21

3. Comparative Analysis of Deep Learning Architectures for Rice Crop Image Classification;Information Systems Engineering and Management;2024

4. Resnet Transfer Learning For Enhanced Medical Image Classification In Healthcare;2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI);2023-12-29

5. Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes;Frontiers in Medicine;2023-12-20

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