An efficient brain tumor image segmentation based on deep residual networks (ResNets)

Shehab, Lamia H.; Fahmy, Omar M.; Gasser, Safa M.; El-Mahallawy, Mohamed S.

doi:10.1016/j.jksues.2020.06.001

Cited by 88 publications

(41 citation statements)

References 16 publications

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“…We used the score of Dice Similarity Coefficient (DSC) to evaluated the model. DSC frequently used for comparing the segmentation result and ground truth [28]. The DSC equation was donated as Eq.…”

Section: Comparisons and Discussionmentioning

confidence: 99%

Residual Fully Convolutional Network for Mandibular Canal Segmentation

2021

IJIES

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Section: Comparisons and Discussionmentioning

confidence: 99%

Residual Fully Convolutional Network for Mandibular Canal Segmentation

2021

IJIES

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“…The receptive field size of the 1 × 1 pixel point on the second feature map corresponds to the input map is 1 × 5. In classical two-dimensional convolutional neural networks such as VGG 33 and ResNet, 34 multiple small-size convolutional kernels are used to replace large-size convolutional kernels for feature extraction. For example, two 3 × 3 convolutional kernels are used to replace a 5 × 5 convolutional kernel to obtain a receptive field of the same size.…”

Section: The Proposed Methodsmentioning

confidence: 99%

A wide kernel CNN-LSTM-based transfer learning method with domain adaptability for rolling bearing fault diagnosis with a small dataset

Zhu¹,

chen

Wei

et al. 2022

Advances in Mechanical Engineering

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It is difficult to obtain sufficient data for some machines, in addition, different working conditions result in different distributions of training data and test data, which lead to the failure of traditional deep learning methods in engineering applications. To solve these problems, we propose a novel deep learning framework called 1D-WCLT for rolling bearing fault diagnosis that combines wide kernel deep convolutional neural network and long short-term memory (WDCNN-LSTM). In this approach, a wide convolution kernel is utilized for local convolution, because the local convolution receptive field is increased, the fault feature contained in the low-middle frequency component is extracted. It is worth mentioning that the network complexity has not increased. Then, the transfer learning is applied to solve related but different domain problems, the useful knowledge learned by WDCNN-LSTM model under sufficient data conditions is transferred to diagnosis tasks with small dataset. After that, some state-of-the-art methods are applied to compare with the proposed method. At the end, experimental results showed that the proposed approach is an excellent algorithm for fault feature extraction of machinery and has much better identification accuracy and applicability than the other existing techniques.

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“…Apart from these models, backbone-based encoders are also utilized in the semantic segmentation tasks. 17,18 These models make use of efficient CNNs like VGG, ResNet, MobileNet, InceptionNet, EfficientNet, etc. as the encoders.…”

Section: Related Workmentioning

confidence: 99%

“…ResNet18 is the encoder in the original LinkNet, which is quite a light but efficient network. Apart from these models, backbone‐based encoders are also utilized in the semantic segmentation tasks 17,18 . These models make use of efficient CNNs like VGG, ResNet, MobileNet, InceptionNet, EfficientNet, etc.…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based semantic segmentation of interphase cells and debris from metaphase images

Sathyan

Menon

Prasad³

et al. 2022

Int J Imaging Syst Tech

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Segmentation plays an essential role in the design of the automated karyotyping system (AKS). It is pivotal to segment interphase cells and other debris usually found in the input G metaphase images. The performance of AKSs is considerably less when interphase cells and debris are present in the input images. In this article, two semantic segmentation models are proposed. For this experiment, an annotated dataset is generated from the G banded metaphase images which are prepared at Regional Cancer Centre (RCC), Thiruvananthapuram, Kerala, India. Inspired by the results of UNet, a lighter version L‐UNet is developed and experimented with. It shows the validation IoU (Intersection over Union) of 0.9809 and F1‐score of 0.9903 on the RCC dataset and the test IoU of 0.9720 and F1‐score of 0.9858 on the CRCN‐NE dataset. As backbone semantic segmentation models are state of the art, an efficient model, Eff‐UNet, is also proposed here. In this model, EfficientNetB03 acts as the backbone that extracts powerful features and UNet acts as the decoder that predicts the segmentation map. It performs with the validation IoU of 0.9842 and F1‐score of 0.9920 on the RCC dataset and the test IoU of 0.7545 and F1‐score of 0.7778 on the CRCN‐NE dataset. To derive this model, 25 encoder–decoder architectures are evaluated with various top‐performing CNNs (convolutional neural networks) as encoders and segmentation networks as decoders. Results are further compared with various segmentation models and the best results are obtained from the proposed model.

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An efficient brain tumor image segmentation based on deep residual networks (ResNets)

Cited by 88 publications

References 16 publications

Residual Fully Convolutional Network for Mandibular Canal Segmentation

Residual Fully Convolutional Network for Mandibular Canal Segmentation

A wide kernel CNN-LSTM-based transfer learning method with domain adaptability for rolling bearing fault diagnosis with a small dataset

Deep learning‐based semantic segmentation of interphase cells and debris from metaphase images

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