Optimized U-Net Segmentation and Hybrid Res-Net for Brain Tumor MRI Images Classification

Rajaragavi, R.; Rajan, S. Palanivel

doi:10.32604/iasc.2022.021206

Cited by 15 publications

(4 citation statements)

References 21 publications

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“…The authors demonstrated that the proposed algorithm achieved good precision compared to similar algorithms and improved the classification of training pixels and lesion localization accuracy. Finally, in this paper ( Rajaragavi & Rajan, 2022 ), the authors proposed a new hybrid model called “ConvLSTM-UNet” for the automatic detection of pneumonia from chest X-ray images. The authors demonstrated that the proposed model outperformed several state-of-the-art models for pneumonia detection in terms of accuracy, sensitivity, specificity, and AUC.…”

Section: Literature Reviewmentioning

confidence: 99%

New hybrid segmentation algorithm: UNet-GOA

Yousefi,

Aktaş

2023

PeerJ Computer Science

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The U-Net architecture is a prominent technique for image segmentation. However, a significant challenge in utilizing this algorithm is the selection of appropriate hyperparameters. In this study, we aimed to address this issue using an evolutionary approach. We conducted experiments on four different geometric datasets (triangle, kite, parallelogram, and square), with 1,000 training samples and 200 test samples. Initially, we performed image segmentation without the evolutionary approach, manually adjusting the U-Net hyperparameters. The average accuracy rates for the geometric images were 0.94463, 0.96289, 0.96962, and 0.93971, respectively. Subsequently, we proposed a hybrid version of the U-Net architecture, incorporating the Grasshopper Optimization Algorithm (GOA) for an evolutionary approach. This method automatically discovered the optimal hyperparameters, resulting in improved image segmentation performance. The average accuracy rates achieved by the proposed method were 0.99418, 0.99673, 0.99143, and 0.99946, respectively, for the geometric images. Comparative analysis revealed that the proposed UNet-GOA approach outperformed the traditional U-Net architecture, yielding higher accuracy rates.

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Section: Literature Reviewmentioning

confidence: 99%

New hybrid segmentation algorithm: UNet-GOA

Yousefi,

Aktaş

2023

PeerJ Computer Science

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“…Then, the feature F t e−256 is fed into the GPG module to restrain the local segmentation errors of low-level feature space. Besides, we aggregate the information progressively from the top level to the low level like the U-Net architecture [26] through a simple feature fusion operation. At last, as mentioned in Section 4, an MEI module is used to refine the coarse segmentation results.…”

Section: Overview Of Global Position Guidance and Multi-path Explicit...mentioning

confidence: 99%

Optimized Deep Learning Model for Fire Semantic Segmentation

Li¹,

Liu²,

Yan³

et al. 2022

Computers, Materials &Amp; Continua

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Recent convolutional neural networks (CNNs) based deep learning has significantly promoted fire detection. Existing fire detection methods can efficiently recognize and locate the fire. However, the accurate flame boundary and shape information is hard to obtain by them, which makes it difficult to conduct automated fire region analysis, prediction, and early warning. To this end, we propose a fire semantic segmentation method based on Global Position Guidance (GPG) and Multi-path explicit Edge information Interaction (MEI). Specifically, to solve the problem of local segmentation errors in low-level feature space, a top-down global position guidance module is used to restrain the offset of low-level features. Besides, an MEI module is proposed to explicitly extract and utilize the edge information to refine the coarse fire segmentation results. We compare the proposed method with existing advanced semantic segmentation and salient object detection methods. Experimental results demonstrate that the proposed method achieves 94.1%, 93.6%, 94.6%, 95.3%, and 95.9% Intersection over Union (IoU) on five test sets respectively which outperforms the suboptimal method by a large margin. In addition, in terms of accuracy, our approach also achieves the best score.

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“…ResNet is known for its residual connections and achieves high accuracy in tasks such as brain tumor segmentation [ 18 ]. U-Net precise localization has been extensively applied to segment brain MR images [ 19 ]. Many researchers have combined ResNet and U-Net to leverage their respective strengths for enhanced accuracy and precision [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks

Azam,

Tariq,

Shehzad

et al. 2023

Brain Sciences

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This research comprises experiments with a deep learning framework for fully automating the skull stripping from brain magnetic resonance (MR) images. Conventional techniques for segmentation have progressed to the extent of Convolutional Neural Networks (CNN). We proposed and experimented with a contemporary variant of the deep learning framework based on mask region convolutional neural network (Mask–RCNN) for all anatomical orientations of brain MR images. We trained the system from scratch to build a model for classification, detection, and segmentation. It is validated by images taken from three different datasets: BrainWeb; NAMIC, and a local hospital. We opted for purposive sampling to select 2000 images of T1 modality from data volumes followed by a multi-stage random sampling technique to segregate the dataset into three batches for training (75%), validation (15%), and testing (10%) respectively. We utilized a robust backbone architecture, namely ResNet–101 and Functional Pyramid Network (FPN), to achieve optimal performance with higher accuracy. We subjected the same data to two traditional methods, namely Brain Extraction Tools (BET) and Brain Surface Extraction (BSE), to compare their performance results. Our proposed method had higher mean average precision (mAP) = 93% and content validity index (CVI) = 0.95%, which were better than comparable methods. We contributed by training Mask–RCNN from scratch for generating reusable learning weights known as transfer learning. We contributed to methodological novelty by applying a pragmatic research lens, and used a mixed method triangulation technique to validate results on all anatomical modalities of brain MR images. Our proposed method improved the accuracy and precision of skull stripping by fully automating it and reducing its processing time and operational cost and reliance on technicians. This research study has also provided grounds for extending the work to the scale of explainable artificial intelligence (XAI).

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Optimized U-Net Segmentation and Hybrid Res-Net for Brain Tumor MRI Images Classification

Cited by 15 publications

References 21 publications

New hybrid segmentation algorithm: UNet-GOA

New hybrid segmentation algorithm: UNet-GOA

Optimized Deep Learning Model for Fire Semantic Segmentation

Fully Automated Skull Stripping from Brain Magnetic Resonance Images Using Mask RCNN-Based Deep Learning Neural Networks

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