Fetal Weight Estimation via Ultrasound Using Machine Learning

Feng, Miao Guo; Wan, Li; Li, Zhi; Qing, Linbo; Qi, Xiaorong

doi:10.1109/access.2019.2925803

Cited by 21 publications

(14 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multiple performance metrics were used to evaluate the results obtained from each algorithm. For example, the weight estimation MAE and MAPE were used 17 , 18 , 49 . Similarly, for LBW classification, several performance metrics such as accuracy, precision, recall, F-score, and confusion matrix were considered 4 .…”

Section: Proposed Methodologymentioning

confidence: 99%

“…Most previous studies that investigate infant BW estimation and LBW classification employ ML algorithms. Feng et al 17 proposed an SVM-based classification model built using a dynamic Bayesian network (DBN) for fetal weight estimation from ultrasound parameters. The authors used a dataset collected from 7875 women with a singleton fetus in West China Secondary Hospital.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms

Khan

Masud

Zaki

et al. 2022

Sci Rep

View full text Add to dashboard Cite

Accurate prediction of a newborn’s birth weight (BW) is a crucial determinant to evaluate the newborn’s health and safety. Infants with low BW (LBW) are at a higher risk of serious short- and long-term health outcomes. Over the past decade, machine learning (ML) techniques have shown a successful breakthrough in the field of medical diagnostics. Various automated systems have been proposed that use maternal features for LBW prediction. However, each proposed system uses different maternal features for LBW classification and estimation. Therefore, this paper provides a detailed setup for BW estimation and LBW classification. Multiple subsets of features were combined to perform predictions with and without feature selection techniques. Furthermore, the synthetic minority oversampling technique was employed to oversample the minority class. The performance of 30 ML algorithms was evaluated for both infant BW estimation and LBW classification. Experiments were performed on a self-created dataset with 88 features. The dataset was obtained from 821 women from three hospitals in the United Arab Emirates. Different performance metrics, such as mean absolute error and mean absolute percent error, were used for BW estimation. Accuracy, precision, recall, F-scores, and confusion matrices were used for LBW classification. Extensive experiments performed using five-folds cross validation show that the best weight estimation was obtained using Random Forest algorithm with mean absolute error of 294.53 g while the best classification performance was obtained using Logistic Regression with SMOTE oversampling techniques that achieved accuracy, precision, recall and F1 score of 90.24%, 87.6%, 90.2% and 0.89, respectively. The results also suggest that features such as diabetes, hypertension, and gestational age, play a vital role in LBW classification.

show abstract

Section: Proposed Methodologymentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms

Khan

Masud

Zaki

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In addition, they found that Abdominal Circumference (AC) and Estimated Fetal Weight (EFW) in week 36 or later are primary predictors of the newborn's BMI. Similarly, Feng et al [46] presented a model for EFW based on SVM and Deep Belief Network (DBN) models. The number of pregnant women who have participated in the study is 7875, including High Birth Weight (HBW) and Low Birth Weight (LBW) fetuses.…”

Section: Fetal Healthmentioning

confidence: 99%

Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities

Khan

Aslam

Anis

et al. 2022

Sensors

View full text Add to dashboard Cite

A fetal ultrasound (US) is a technique to examine a baby’s maturity and development. US examinations have varying purposes throughout pregnancy. Consequently, in the second and third trimester, US tests are performed for the assessment of Amniotic Fluid Volume (AFV), a key indicator of fetal health. Disorders resulting from abnormal AFV levels, commonly referred to as oligohydramnios or polyhydramnios, may pose a serious threat to a mother’s or child’s health. This paper attempts to accumulate and compare the most recent advancements in Artificial Intelligence (AI)-based techniques for the diagnosis and classification of AFV levels. Additionally, we provide a thorough and highly inclusive breakdown of other relevant factors that may cause abnormal AFV levels, including, but not limited to, abnormalities in the placenta, kidneys, or central nervous system, as well as other contributors, such as preterm birth or twin-to-twin transfusion syndrome. Furthermore, we bring forth a concise overview of all the Machine Learning (ML) and Deep Learning (DL) techniques, along with the datasets supplied by various researchers. This study also provides a brief rundown of the challenges and opportunities encountered in this field, along with prospective research directions and promising angles to further explore.

show abstract

“…It could severely hinder the classifier's ability to recognise the weed-infested regions. In order to address the issue, we implement and compare two different sampling techniques to increase the frequency of weed plant samples as for most imbalanced data sets, the application of sampling techniques does indeed aid in improved classifier accuracy [63]. Firstly, a combination of random oversampling (resample certain data points from the minority class) and undersampling (drops data points from the majority class) is used while training the model, thus increasing the ratio of weed tile samples in the dataset.…”

Section: A: Feature Vector Based Classificationmentioning

confidence: 99%

Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning

et al. 2021

View full text Add to dashboard Cite

Uncontrolled growth of weeds can severely affect the crop yield and quality. Unrestricted use of herbicide for weed removal alters biodiversity and cause environmental pollution. Instead, identifying weed-infested regions can aid selective chemical treatment of these regions. Advances in analyzing farm images have resulted in solutions to identify weed plants. However, a majority of these approaches are based on supervised learning methods which requires huge amount of manually annotated images. As a result, these supervised approaches are economically infeasible for the individual farmer because of the wide variety of plant species being cultivated. In this paper, we propose a deep learning-based semi-supervised approach for robust estimation of weed density and distribution across farmlands using only limited color images acquired from autonomous robots. This weed density and distribution can be useful in a site-specific weed management system for selective treatment of infected areas using autonomous robots. In this work, the foreground vegetation pixels containing crops and weeds are first identified using a Convolutional Neural Network (CNN) based unsupervised segmentation. Subsequently, the weed infected regions are identified using a fine-tuned CNN, eliminating the need for designing hand-crafted features. The approach is validated on two datasets of different crop/weed species (1) Crop Weed Field Image Dataset (CWFID), which consists of carrot plant images and the (2) Sugar Beets dataset. The proposed method is able to localize weed-infested regions a maximum recall of 0.99 and estimate weed density with a maximum accuracy of 82.13%. Hence, the proposed approach is shown to generalize to different plant species without the need for extensive labeled data.

show abstract

Fetal Weight Estimation via Ultrasound Using Machine Learning

Cited by 21 publications

References 28 publications

Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms

Infant birth weight estimation and low birth weight classification in United Arab Emirates using machine learning algorithms

Amniotic Fluid Classification and Artificial Intelligence: Challenges and Opportunities

Weed Density and Distribution Estimation for Precision Agriculture Using Semi-Supervised Learning

Contact Info

Product

Resources

About