Brain Tumor Segmentation Using Deep Capsule Network and Latent-Dynamic Conditional Random Fields

Elmezain, Mahmoud; Mahmoud, Amena; Mosa, Diana T.; Said, Wael

doi:10.3390/jimaging8070190

Cited by 17 publications

(4 citation statements)

References 34 publications

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“…Mahmoud Elmezain [20] [22] implemented a hybrid technique based on deep convolutional neural network (DCNN) and ML for segmentation and classification of tumors by utilizing MRI. The CNN is in the initial stage of learning the feature map from the space of image of MRI images into the region of the tumor marker.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This section shows the comparative analysis of the CNN classifier with performance metrics like accuracy, precision and recall as shown in Table 4, 5 and 6. Existing methods like 3D U-Net [19], CapsNet + LDCRF + post-processing [20], CNN [24], Ensemble Transfer learning and Quantum Variational Classifier [25], Softmax classifier [26] and Mathematical model with 3D attention U-net [27] are used for evaluating the ability of this classifier. The proposed model is trained, tested and validated by using BRAST 2019, 2020 and 2021 dataset.…”

Section: Comparative Analysismentioning

confidence: 99%

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Batch Normalization Based Convolutional Neural Network for Segmentation and Classification of Brain Tumor MRI Images

2024

IJIES

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The uncontrolled growth of cells in human brain can lead to the formation of tumors, which can occur in all age people. The tumor in brain can affect nerve cells, soft tissues and blood vessels. The early detection of brain tumor is necessary to aid doctors in treating cancer patients to increase their survival rate. For this various deep learning models are created and discovered for efficient brain tumor detection and classification. In this paper, the Convolutional Neural Network is proposed for efficient brain tumor classification in MRI images using BRATS 2019, 2020 and 2021 dataset. The min-max normalization is used in this research for data preprocessing and fed to the segmentation process. The mask region-based CNN is employed for segmenting brain tumors; Followed by that, Batch normalization is applied to enhance the training process and minimize the overfitting issues. The obtained result shows that the proposed CNN model achieves better accuracy of 99.55% on BRATS 2019, 99.80% on BRATS 2020 and 99.29% on BRATS 2021 dataset which ensures accurate classification compared with other existing methods like 3D U-Net and CapsNet + latent-dynamic condition random field (LDCRF) + post-processing.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Comparative Analysismentioning

confidence: 99%

Batch Normalization Based Convolutional Neural Network for Segmentation and Classification of Brain Tumor MRI Images

2024

IJIES

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“…Along the same lines, Elmezain et al [25] introduced an ensemble model for automatic brain tumor segmentation, which combines a latent-dynamic conditional random field with a deep capsule network. The approach involves three distinct procedures: image pre-processing, image segmentation, and image post-processing.…”

Section: Related Workmentioning

confidence: 99%

Brain Tumor Detection Using 3D-UNet Segmentation Features and Hybrid Machine Learning Model

Mallampati,

Ishaq,

Rustam

et al. 2023

IEEE Access

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Machine learning has significantly improved disease diagnosis, enhancing the efficiency and accuracy of the healthcare system. One critical area where it proves beneficial is diagnosing brain tumors, a life-threatening disease, where early and accurate predictions can save lives. This study focuses on deploying a machine learning-based approach for brain tumor detection, utilizing Magnetic Resonance Imaging (MRI) features. We train the proposed model using 3D-UNet and 2D-UNet segmentation features extracted from MRI, encompassing shape, statistics, gray level size zone matrix, gray level dependence matrix, gray level co-occurrence matrix, and gray level run length matrix values. To improve performance, we propose a hybrid model that combines the strengths of two machine learning models, K-nearest neighbor (KNN) and gradient boosting classifier (GBC), using soft voting criteria. We combine them because, in cases where KNN exhibits poor performance for certain data points, GBC demonstrates significant performance, and vice versa, where GBC shows poor results, KNN performs significantly better. With 2D-UNet segmentation features, the model achieves a 64% accuracy. By training it on 3D-UNet segmentation features, we achieve a significant accuracy of 71% which surpasses existing state-of-the-art models that utilize 3D-UNet segmentation features.

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“…Therefore, numerous research studies have been conducted on developing techniques for automatic segmentation. For instance, Elmezain et al [6] employed convolutional neural networks (CNNs) to segment the images and then used the latent dynamic conditional random field (LDCRF) to optimize the segmentation results, thereby improving the accuracy of BG segmentation. In general, CNN-based segmentation models can achieve good results in image segmentation tasks.…”

Section: Introductionmentioning

confidence: 99%

WD‐UNeXt: Weight loss function and dropout U‐Net with ConvNeXt for automatic segmentation of few shot brain gliomas

Yin

Gao

Gong

et al. 2023

IET Image Processing

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Accurate segmentation of brain gliomas (BG) is a crucial and challenging task for effective treatment planning in BG therapy. This study presents the weight loss function and dropout U‐Net with ConvNeXt block (WD‐UNeXt), which precisely segments BG from few shot MRI. The ConvNeXt block, which comprises the main body of the network, is a structure that can extract more detailed features from images. The weight loss function addresses the issue of category imbalance, thereby enhancing the network's ability to achieve more precise segmentation. The training set of BraTS2019 was used to train the network and apply it to test data. Dice similarity coefficient (DSC), sensitivity (Sen), specificity (Spec) and Hausdorff distance (HD) were used to assess the performance of the method. The experimental results demonstrate that the DSC of whole tumour, tumour core and enhancing tumour reached 0.934, 0.911 and 0.851, respectively. Sen of the sub‐regions achieved 0.922, 0.911 and 0.867. Spec and HD reached 1.000, 1.000, 1.000 and 3.224, 2.990, 2.844, respectively. Compared with the performance of state‐of‐the‐art methods, the DSC and HD of WD‐UNeXt were improved to varying degrees. Therefore, this method has considerable potential for the segmentation of BG.

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Brain Tumor Segmentation Using Deep Capsule Network and Latent-Dynamic Conditional Random Fields

Cited by 17 publications

References 34 publications

Batch Normalization Based Convolutional Neural Network for Segmentation and Classification of Brain Tumor MRI Images

Batch Normalization Based Convolutional Neural Network for Segmentation and Classification of Brain Tumor MRI Images

Brain Tumor Detection Using 3D-UNet Segmentation Features and Hybrid Machine Learning Model

WD‐UNeXt: Weight loss function and dropout U‐Net with ConvNeXt for automatic segmentation of few shot brain gliomas

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