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3D-UNet Architecture Using Separable 2D Convolutions
2020
International Journal of Computing Communications and Networking
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Even though many manual segmentations and magnetic resonance image has emerged they are highly time consuming and error prone.2D and 3D convolutions using neural networks cannot satisfy the whole treating ...
Accuracy in quantitative analysis and segmentation of brain are crucial for the treatment sketch. ...
UNet based deep convolutional networks UNets are algorithm used for image segmentation in medical field. ...
doi:10.30534/ijccn/2020/08922019
fatcat:47tqpp5gordmto6nkrlylo54qm
Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges
2020
Brain Sciences
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In recent years, usage of deep learning is rapidly proliferating in almost every domain, especially in medical image processing, medical image analysis, and bioinformatics. ...
A critical discussion section to show the limitations of deep learning techniques has been included at the end to elaborate open research challenges and directions for future work in this emergent area ...
The Capsule Network and Generative Adversarial Network (cGAN) has also been used for medical image analysis in various articles. ...
doi:10.3390/brainsci10020118
pmid:32098333
pmcid:PMC7071415
fatcat:wofq4puvcbemlconbz6carsf2y
Deep Neural Architectures for Medical Image Semantic Segmentation: Review
2021
IEEE Access
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V-Net, a volumetric convolutional neural network, which is FCN for Volumetric Medical Image Segmentation, was proposed in [143] . ...
DEEP NEURAL NETWORK ARCHITECTURES 1) Convolution neural networks (CNNs) A convolutional neural network (CNN) is an advanced neural network architecture developed for analyzing twodimensional images [50 ...
doi:10.1109/access.2021.3086530
fatcat:hacpqwdxybh63j5ygebqszm7qq
Dense Convolutional Network and Its Application in Medical Image Analysis
2022
BioMed Research International
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Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. ...
the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. ...
Application of DenseNet in Medical Image Segmentation. ...
doi:10.1155/2022/2384830
pmid:35509707
pmcid:PMC9060995
fatcat:7jp3tmtph5hk5gthgcomeccnte
Enhanced CNN Based Electron Microscopy Image Segmentation
2012
Cybernetics and Information Technologies
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This paper proposes an approach using perceptual grouping via a graph cut and its combinations with Convolutional Neural Network (CNN) to achieve improved segmentation of SEM images. ...
Detecting the neural processes like axons and dendrites needs high quality SEM images. ...
; Convolutional Neural Network can produce an affinity graph which is suitable for segmentation. ...
doi:10.2478/cait-2012-0014
fatcat:bc3ezrw4rzcyzj76khsqqnk674
A Review of Deep-Learning-Based Medical Image Segmentation Methods
2021
Sustainability
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For example, the segmentation accuracy is not high, the number of medical images in the data set is small and the resolution is low. ...
With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot. ...
The GAN-based data enhancement technology for segmentation tasks is widely used in different medical images. ...
doi:10.3390/su13031224
fatcat:pn2qbyv53zbuhhiuem2pc4dg3u
Using Capsule Neural Network to predict Tuberculosis in lens-free microscopic images
[article]
2020
arXiv
pre-print
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We employ the CapsNet architecture in our collected dataset and show that it has a better accuracy than traditional CNN architectures. ...
This work seeks to facilitate and automate the prediction of tuberculosis by the MODS method and using lens-free microscopy, which is easy to use by untrained personnel. ...
In recent years Convolutional Neural Networks (CNNs), a class of deep neural networks, have become the state-of-theart for object recognition in computer vision (Krizhevsky et al., 2012) , and have potential ...
arXiv:2007.02457v1
fatcat:4zaulnjdavd4np7vfyzh262pde
Kidney Tumor Semantic Segmentation Using Deep Learning: A Survey of State-of-the-Art
2022
Journal of Imaging
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Deep learning (DL), particularly convolutional neural networks, has produced outstanding success in classifying and segmenting images. ...
We discuss the various types of medical images and segmentation techniques and the assessment criteria for segmentation outcomes in kidney tumor segmentation, highlighting their building blocks and various ...
Convolutional Neural Network (CNN) A CNN is a kind of neural network. It was first released in 1980 [75] . ...
doi:10.3390/jimaging8030055
pmid:35324610
pmcid:PMC8954467
fatcat:7dhh3zwk5zcmpe3ijzbgpmo4ze
BE-FNet: 3D Bounding Box Estimation Feature Pyramid Network for Accurate and Efficient Maxillary Sinus Segmentation
2020
Mathematical Problems in Engineering
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but eliminate false positives remarkably, promoting 3D segmentation applied in 3D convolutional neural networks. ...
However, for 3D segmentation in medical images, 2D networks are not competent in extracting more significant spacial features, and 3D ones suffer from unbearable burden of computation, which results in ...
Recently, fully convolutional neural networks (FCNs) have achieved great success on a broad array of segmentation problems in medical images [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [29] ...
doi:10.1155/2020/5689301
fatcat:eyqyyntn4vdphgf26cgxkuz2e4
Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning
2021
Eastern-European Journal of Enterprise Technologies
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This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable ...
accuracy for medical-grade application. ...
U-Net Convolutional Neural Network (CNN) model is typically used for image segmentation. U-Net CNN model is a customized network, which has been introduced originally in [11] . ...
doi:10.15587/1729-4061.2021.238957
fatcat:widnihwzobflhkrgha7mlat7fq
Artificial Neural Network-Based Automated Crack Detection and Analysis for the Inspection of Concrete Structures
2020
Applied Sciences
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In the first and second stages, two-dimensional convolutional neural networks are used for crack image detection (e.g., classification and segmentation). ...
Based on convolution neural network for the detection, hierarchical feature learning architecture is applied into our deep learning network. ...
used for medical image segmentation. ...
doi:10.3390/app10228105
fatcat:q33x7mdborh6pctmkv3c5wejaa
Deep Learning Assisted Image Interactive Framework For Brain Image Segmentation
2020
IEEE Access
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INDEX TERMS Convolutional neural networks (CNN), image-specific fine tuning, geodesic transforms, deep learning. ...
The Convolutional Neural Networks (CNN) has been developed by the efficient auto segmentation technology. In fact, the clinical outcomes are not appropriately specific and detailed. ...
VOLUME 8, 2020 FIGURE 1 . 1 Deep learning with medical image processing.
FIGURE 2 . 2 Example of convolutional neural networks in deep learning. ...
doi:10.1109/access.2020.3003624
fatcat:h4pwbs324zg2rg6quliecbc3fe
Predicting the Existence of Brain Tumor in Mri Images by Applying FCNN
2020
Medico-Legal Update
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The objective of this project is to detect the tumors early from MR image scans by utilizing deep convolutional networks to locate the tumor.The tumor is divided at first in the main stage and the generated ...
bounding box is utilized for the center of the tumor in second step. ...
In this work, Ren et al. proposed a methodology called Region Proposal Network which offers full-image convolutional features with the discovery network enabling nearly cost-free region. 11 An RPN is ...
doi:10.37506/mlu.v20i3.1426
fatcat:6jjldlkwg5aopnggr2pwpeztvi
CISP-BMEI 2020 TOC
2020
2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
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........................................................................175 Hyperspectral Remote Sensing Image Segmentation Based on Fuzzy Deep Convolutional Neural Network
XiaoyingWei, Yanhua Cao ...
for medical support applications Dimitris Kastaniotis, Dimitrios Tsourounis, Spiros Fotopoulos ...
....366 Alterations of Brain Functional Networks in Older Adults: A Resting-state fMRI Study Using ...
doi:10.1109/cisp-bmei51763.2020.9263536
fatcat:7ulpvhnt35d2lg5dwzu4kexley
Finger vein verification algorithm based on Fully Convolutional Neural Network and Conditional Random Field
2020
IEEE Access
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With the steps mentioned above, the fully convolutional neural network is constructed. ...
This paper, so as to compensate this defect, proposes an end-to-end model to extract vein textures through integrating the fully convolutional neural network (FCN) with conditional random field (CRF). ...
The segmentation method based on fully convolutional neural network provides advanced results for image semantic segmentation [49] . ...
doi:10.1109/access.2020.2984711
fatcat:txs7iv53xzf7lkim4khebnet7u
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