Infant brain segmentation based on a combination of VGG‐16 and U‐Net deep neural networks

Pasban, Sadegh; Mohamadzadeh, Sajad; Zeraatkar-Moghaddam, Javad; Shafiei, Ali

doi:10.1049/iet-ipr.2020.0469

Cited by 12 publications

(10 citation statements)

References 26 publications

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“…VGG, which is a deep CNN, performs better than the baseline on a variety of tasks and datasets, not just ImageNet. The name of the network, VGG, gives away the fact that it is a deep neural network consisting of between 16 and 19 layers [26].…”

Section: Vggmentioning

confidence: 99%

Covid-19 and Pneumonia Infection Detection from Chest X-Ray Images using U-Net, EfficientNetB1, XGBoost and Recursive Feature Elimination

Lunagaria¹,

Katkar²,

Vaghela³

2022

IJACSA

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The pandemic caused by the COVID-19 virus is the most serious current threat to the public's health. For the purpose of identifying patients with Covid-19, Chest X-Rays have proven to be an indispensable imaging modality for the hospital. Nevertheless, radiologists are needed to commit a significant amount of time to their interpretation. It is possible to diagnose and triage cases of Covid-19 effectively and rapidly with the assistance of precise computer systems that are powered by Machine Learning techniques. Machine Learning techniques such as Deep Feature Extraction can help detect the disease with improved precision and speed when used in conjunction with X-Ray images of the lung. This helps to alleviate the problem of lack of testing kits. Using the U-Net for Semantic image segmentation for lung segmentation and deep feature extractionbased strategy that was suggested in this research, it is possible to differentiate between patients who have contracted the Covid-19 virus, pneumonia and healthy people. XGBoost and recursive feature extraction based proposed methodology is evaluated using 20 different Pre-Trained deep learning based models including EfficientNet variations and it is observed that the maximum detection accuracy, precision, recall specificity, and F1-score are achieved when EfficientNetB1 is used to extract deep features. The respective values for these metrics are 97.6%, 0.964, 0.964, and 0.982. These findings lend credence to the efficiency of the proposed methodology.

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Section: Vggmentioning

confidence: 99%

Covid-19 and Pneumonia Infection Detection from Chest X-Ray Images using U-Net, EfficientNetB1, XGBoost and Recursive Feature Elimination

Lunagaria¹,

Katkar²,

Vaghela³

2022

IJACSA

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show abstract

“…Pooling layer, also named as subsampling or downsampling, is often used behind of convolutional layer in a classic architecture of CNN [15]. Its main purposes include reducing feature dimension of convolutional layer output [16], suppressing noise, reducing quantity of parameters and computation cost, and dampening overfitting [17].…”

Section: Convolutional Neural Network Pooling Layermentioning

confidence: 99%

Multiple Sclerosis Detection via 6-layer Stochastic Pooling Convolutional Neural Network and Multiple-way Data Augmentation

Wang

Lima

2021

STEMedicine

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Multiple sclerosis is one of most widespread autoimmune neuroinflammatory diseases which mainly damages body function such as movement, sensation, and vision. Despite of conventional clinical presentation, brain magnetic resonance imaging of white matter lesions is often applied to diagnose multiple sclerosis at the early stage. In this article, we proposed a 6-layer stochastic pooling convolutional neural network with multiple-way data augmentation for multiple sclerosis detection in brain MRI images. Our approach does not demand hand-crafted features unlike those traditional machine learning methods. Via application of stochastic pooling and multiple-way data augmentation, our 6-layer CNN achieved equivalent performance against those deep learning methods which consist of so many layers and parameters that ordinarily bring difficulty to training. The results showed that this 6-layer CNN obtained a sensitivity of 95.98±0.46%, a specificity of 95.67±0.92%, and an accuracy of 95.82±0.58%. According to comparison experiments, our results are better than state-of-the-art approaches. Further, we also conducted ablation experiments to examine the contribution of stochastic pooling and multiple-way data augmentation to the original CNN model. The contrast experiments revealed that our scheme of stochastic pooling and multiple-way data augmentation enhanced the original 6-layer CNN model compared to those using maximum pooling or average pooling and inadequate data augmentation.

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“…The precise segmentation of brain tumor regions is an essential basis for clinicians to formulate surgical plans, radiotherapy plans, and pathological examinations. The research of automatic and accurate brain tumor segmentation based on deep learning has made important progress [ 1 , 2 ]. The improved model based on a fully convolutional network (FCN) [ 3 ] and U-Net [ 4 ] benchmark network is one of the important research directions.…”

Section: Introductionmentioning

confidence: 99%

Improved U-Net3+ with stage residual for brain tumor segmentation

et al. 2022

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Background For the encoding part of U-Net3+,the ability of brain tumor feature extraction is insufficient, as a result, the features can not be fused well during up-sampling, and the accuracy of segmentation will reduce. Methods In this study, we put forward an improved U-Net3+ segmentation network based on stage residual. In the encoder part, the encoder based on the stage residual structure is used to solve the vanishing gradient problem caused by the increasing in network depth, and enhances the feature extraction ability of the encoder which is instrumental in full feature fusion when up-sampling in the network. What’s more, we replaced batch normalization (BN) layer with filter response normalization (FRN) layer to eliminate batch size impact on the network. Based on the improved U-Net3+ two-dimensional (2D) model with stage residual, IResUnet3+ three-dimensional (3D) model is constructed. We propose appropriate methods to deal with 3D data, which achieve accurate segmentation of the 3D network. Results The experimental results showed that: the sensitivity of WT, TC, and ET increased by 1.34%, 4.6%, and 8.44%, respectively. And the Dice coefficients of ET and WT were further increased by 3.43% and 1.03%, respectively. To facilitate further research, source code can be found at: https://github.com/YuOnlyLookOne/IResUnet3Plus. Conclusion The improved network has a significant improvement in the segmentation task of the brain tumor BraTS2018 dataset, compared with the classical networks u-net, v-net, resunet and u-net3+, the proposed network has smaller parameters and significantly improved accuracy.

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Infant brain segmentation based on a combination of VGG‐16 and U‐Net deep neural networks

Cited by 12 publications

References 26 publications

Covid-19 and Pneumonia Infection Detection from Chest X-Ray Images using U-Net, EfficientNetB1, XGBoost and Recursive Feature Elimination

Covid-19 and Pneumonia Infection Detection from Chest X-Ray Images using U-Net, EfficientNetB1, XGBoost and Recursive Feature Elimination

Multiple Sclerosis Detection via 6-layer Stochastic Pooling Convolutional Neural Network and Multiple-way Data Augmentation

Improved U-Net3+ with stage residual for brain tumor segmentation

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