Nature-Inspired Level Set Segmentation Model for 3D-MRI Brain Tumor Detection

Hassen, Oday A.; Abter, Sarmad Omar; Abdulhussein, Ansam A.; Darwish, Saad M.; Ibrahim, Yasmine Mahmoud; Sheta, Walaa M.

doi:10.32604/cmc.2021.014404

Cited by 5 publications

(6 citation statements)

References 49 publications

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“…17 Oday Ali Hassen et al introduced a model that uses level set segmentation and Artificial Bee Colony optimization to satisfy parameters that could help detect brain tumors. 18 Segmentation of brain tumors was also achieved by Bayesian active learning and GAN networks by Alshehhi and Alshehhi. 19 A model based on multi-scale prediction with 3D U-Net where feature extraction takes place in the encoder part of the network and downsampling by ReLU was proposed by Chen et al 20 Muti-resolution features derived by the decoder were then aggregated to give the final segmentation result.…”

Section: Related Workmentioning

confidence: 99%

“…Each model was deployed for volume prediction and final ensembling 17 . Oday Ali Hassen et al introduced a model that uses level set segmentation and Artificial Bee Colony optimization to satisfy parameters that could help detect brain tumors 18 . Segmentation of brain tumors was also achieved by Bayesian active learning and GAN networks by Alshehhi and Alshehhi 19 …”

Section: Related Workmentioning

confidence: 99%

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Segmentation of brain tumor subregions with depthwise separable dense U‐NET (DSDU‐NET)

Nehru

Prabhu

2023

Int J Imaging Syst Tech

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Brain tumors are one of the most dangerous medical conditions. The dispersed extremities and nonuniform structure of the tumors are the basis of why techniques of traditional segmentation have grown to be inefficient. Magnetic resonance imaging (MRI) is one of the most widely used scanning procedures for tumors. However, to ameliorate the survival rate, the detection of tumors alone does not suffice, and there are other effective procedures. One of the most pivotal procedures for diagnosing the condition is the process of the brain tumor segmentation is a laborious and cumbersome process. As a result, a deep learning (DL) based solution is used to extract tumor subregions like enhancing tumor (ET), tumor core (TC), and whole tumor (WT). The proposed model involves novel DSDU‐Net: Depth‐wise separable dense U‐NET (DSDU‐NET) that precise outputs are acquired by retaining the low‐level features. In the preprocessing stage, a methodology called multi‐scale patch extraction is used to segregate tumor regions, and a grouping of Gaussian filter, unsharp masking, and histogram equalization is carried out on the data set to get significantly better performance. The proposed DSDU‐NET network performed better in terms of performance, specificity, sensitivity, dice similarity index, and Hausdorff distance for segmented image sub‐regions when validated on BraTS 2018 and 2019 data sources when particularly in comparison to other models.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Segmentation of brain tumor subregions with depthwise separable dense U‐NET (DSDU‐NET)

Nehru

Prabhu

2023

Int J Imaging Syst Tech

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“…Our lead paper used RCNN, which enhances segmentation efficiency by computing deep features with a fair representation of Melanoma. The RCNN can identify numerous skin diseases in the same patient and diverse illnesses in separate individuals [48]. We propose a method of Brain tumor detection and segmentation for detecting and segmenting brain tumors using a deep regional convolutional neural network (RCNN) and the Active contour segmentation approach in this study.…”

Section: Deep Learningmentioning

confidence: 99%

Brain Tumor Detection and Segmentation Using RCNN

Khan¹,

Shah²,

Ali³

et al. 2022

Computers, Materials &Amp; Continua

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Brain tumors are considered as most fatal cancers. To reduce the risk of death, early identification of the disease is required. One of the best available methods to evaluate brain tumors is Magnetic resonance Images (MRI). Brain tumor detection and segmentation are tough as brain tumors may vary in size, shape, and location. That makes manual detection of brain tumors by exploring MRI a tedious job for radiologists and doctors'. So an automated brain tumor detection and segmentation is required. This work suggests a Region-based Convolution Neural Network (RCNN) approach for automated brain tumor identification and segmentation using MR images, which helps solve the difficulties of brain tumor identification efficiently and accurately. Our methodology is based on the accurate and efficient selection of tumorous areas. That reduces computational complexity and time. We have validated the designed experimental setup on a standard dataset, BraTS 2020. We used binary evaluation matrices based on Dice Similarity Coefficient (DSC) and Mean Average Precision (mAP). The segmentation results are compared with state-of-the-art methodologies to demonstrate the effectiveness of the proposed method. The suggested approach attained an average DSC of 0.92 and mAP 0.92 for 10 patients, while on the whole dataset, the scores are DSC 0.89 and mAP 0.90. The following results clearly show the performance efficiency of the proposed methodology.

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“…AlexNet [30], VGGNet [31], ResNet [32], and ResNeXt [33] are only few examples of pre-trained image classification networks that have learnt rich feature representations applicable to a broad variety of images. More than a million images from over a thousand item categories are used to train these networks [34][35][36] in the ILSVRC subset of the ImageNet database.…”

Section: Knowledge Transfermentioning

confidence: 99%

Deep Learning Based Hybrid Classifier for Analyzing Hepatitis C in Ultrasound Images

Thjeel

2022

WJCMS

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Although liver biopsy is the gold standard for identifying diffuse liver disorders, it is an intrusive procedure with a host of negative side effects. Physician subjectivity may affect the ultrasonography diagnosis of diffuse liver disease. As a result, there is still a clear need for an appropriate classification of liver illnesses. In this article, an unique deep classifier made up of deep convolutional neural networks (CNNs) that have already been trained is proposed to categories the liver condition. The variants of ResNet and AlexNet are a few networks that are combined with fully connected networks (FCNs). Transfer learning can be used to extract deep features that can offer adequate categorization data. Then, an FCN can depict images of the disease in its many stages, including tissue, liver hepatitis, and hepatitis. To discriminate between these liver images, three different (normal/cirrhosis, perfectly natural, and cirrhosis/hepatitis) and 3 (normal/cirrhosis/hepatitis) models were trained. A hybrid classifier is presented in order to integrate the graded odds of the classes produced by each individual classifier since two-class classifiers performed better than three-class classifiers. The class with the highest score is then chosen using a majority voting technique. The experimental results demonstrate an high accuracy when liver images were divided into three classes using ResNet50 and a hybrid classifier.

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Nature-Inspired Level Set Segmentation Model for 3D-MRI Brain Tumor Detection

Cited by 5 publications

References 49 publications

Segmentation of brain tumor subregions with depthwise separable dense U‐NET (DSDU‐NET)

Segmentation of brain tumor subregions with depthwise separable dense U‐NET (DSDU‐NET)

Brain Tumor Detection and Segmentation Using RCNN

Deep Learning Based Hybrid Classifier for Analyzing Hepatitis C in Ultrasound Images

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