Standard Plane Identification in Fetal Brain Ultrasound Scans Using a Differential Convolutional Neural Network

Health complications during the gestation period have evolved as a global issue. These complications sometimes result in the mortality of the fetus, which is more prevalent in developing and underdeveloped countries. The genesis of machine learning (ML) algorithms in the healthcare domain have brought remarkable progress in disease diagnosis, treatment, and prognosis. This research deploys various ML algorithms to predict fetal health from the cardiotocographic (CTG) data by labelling the health state into normal, needs guarantee, and pathology. This work assesses the influence of various factors measured through CTG to predict the health state of the fetus through algorithms like support vector machine, random forest (RF), multi‐layer perceptron, and K‐nearest neighbours. In addition to this, the regression analysis and correlation analysis revealed the influence of the attributes on fetal health. The results of the algorithms show that RF performs better than its peers in terms of accuracy, precision, recall, F1‐score, and support. This work can further enhance more promising results by performing suitable feature engineering in the CTG data.

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“…This fact can be visualized from Figure 2c, d. Thus, the RA can be done by doctors to forecast the fetal health condition (Qu et al, 2020).…”

Section: Fetal Health Classification From Ctg Datamentioning

confidence: 99%

Fetal health classification from cardiotocographic data using machine learning

et al. 2021

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“…The approach is tested on 2418 images reaching a mean AUC = 0.99 and Acc, P rec, Rec and F 1 of 0.96, 0.96, 0.97, 0.97, respectively. A shallow classification CNN is also used in [27] to automatically identify FBSPs. The dataset consists of 19142 images, which are augmented and split in 5 folds to test the algorithm.…”

Section: B Publicly-available Datasetsmentioning

confidence: 99%

“…From the review of the literature on standard-plane detection, a first limitation emerges in the number of considered planes. There are papers ( [33], [26], [27], [34]) that consider only few planes. In some cases ( [36], [37], [25]) only one plane is considered.…”

Section: A Limitations and Open Issues For Fetal Standard-plane Detec...mentioning

confidence: 99%

A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis

Fiorentino¹,

Villani²,

Cosmo³

et al. 2022

Preprint

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Deep-learning (DL) algorithms are becoming the standard for processing ultrasound (US) fetal images. Despite the large number of survey papers already present in this field, most of them are focusing on a broader area of medicalimage analysis or not covering all fetal US DL applications. This paper surveys the most recent work in the field, with a total of 145 research papers published after 2017. Each paper is analyzed and commented from both the methodology and application perspective. We categorized the papers in (i) fetal standard-plane detection, (ii) anatomical-structure analysis and (iii) biometry parameter estimation. For each category, main limitations and open issues are presented. Summary tables are included to facilitate the comparison among the different approaches. Publicly-available datasets and performance metrics commonly used to assess algorithm performance are summarized, too. This paper ends with a critical summary of the current state of the art on DL algorithms for fetal US image analysis and a discussion on current challenges that have to be tackled by researchers working in the field to translate the research methodology into the actual clinical practice.

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“…Qu et al also used a CNNbased method for the identification of fetal brain ultrasound. They proposed a differential convolutional neural network (differential-CNN) to identify six fetal brains automatically [22] .…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network to Detect and Measure Fetal Skull Circumference in Ultrasound Imaging

et al. 2020

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In obstetrics, ultrasound is used for assessment of fetal development during pregnancy. The images generated by ultrasound are used to obtain measurements of fetal head length, body size, and the analysis of fetal movements, to identify and prevent the onset of congenital disease. This work presents the development of a new method for the segmentation of two-dimensional ultrasound images of fetal skulls based on a V-Net architecture called Fully Convolutional Neural Network-Combination (VNetc). We created a new combination of strategies using a 3D V-Net as base, such as pre-processing, use of Batch Normalization and Dropout, and evaluation of distinct activation layers, activation function, data augmentation, loss function, and network depth. The computational results reveal the feasibility of the proposal in the correct segmentation of fetal skulls and head circumference measurements, reaching up to 97.91% of correctness, overcoming states-of-the-art methods.

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Standard Plane Identification in Fetal Brain Ultrasound Scans Using a Differential Convolutional Neural Network

Cited by 37 publications

References 44 publications

Fetal health classification from cardiotocographic data using machine learning

Fetal health classification from cardiotocographic data using machine learning

A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis

Convolutional Neural Network to Detect and Measure Fetal Skull Circumference in Ultrasound Imaging

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