MRI Brain Tumor Segmentation Using Deep Learning. (Dept. E)

Nassar, Shimaa E.; Mohamed, Mohamed Abd El-Azim; Elnakib, Ahmed

doi:10.21608/bfemu.2021.139470

Cited by 9 publications

(11 citation statements)

References 21 publications

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“…We applied the two models (Densenet and FCN-Alexnet) in the proposed system, since their decoders produce outputs that are of the same dimensions as the input image, which suits the task of segmentation. In addition, they have shown outstanding performance for several related medical applications, such as lung segmentation [17], [18], pulmonary cancerous detection [19], face recognition [20], brain cancer [21] and diabetic retinopathy [22].…”

Section: Methodsmentioning

confidence: 99%

Deep segmentation of the liver and the hepatic tumors from abdomen tomography images

Elmenabawy

El-Seddek

Moustafa

et al. 2022

IJECE

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A pipelined framework is proposed for accurate, automated, simultaneous segmentation of the liver as well as the hepatic tumors from computed tomography (CT) images. The introduced framework composed of three pipelined levels. First, two different transfers deep convolutional neural networks (CNN) are applied to get high-level compact features of CT images. Second, a pixel-wise classifier is used to obtain two output-classified maps for each CNN model. Finally, a fusion neural network (FNN) is used to integrate the two maps. Experimentations performed on the MICCAI’2017 database of the liver tumor segmentation (LITS) challenge, result in a dice similarity coefficient (DSC) of 93.5% for the segmentation of the liver and of 74.40% for the segmentation of the lesion, using a 5-fold cross-validation scheme. Comparative results with the state-of-the-art techniques on the same data show the competing performance of the proposed framework for simultaneous liver and tumor segmentation.

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Section: Methodsmentioning

confidence: 99%

Deep segmentation of the liver and the hepatic tumors from abdomen tomography images

Elmenabawy

El-Seddek

Moustafa

et al. 2022

IJECE

Self Cite

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“…The ambiguity can be decreased, and the accuracy can be significantly increased by combining the tumor data that has been taken from many advanced networks. To gain more precise anatomical data on brain tumors, Nassar et al [ 20 , 21 ] fed the CNN model by integrating the image features of long skip-linked lesions. W. Chen et al [ 22 ] showed a separate 3D U-Net model that got around the memory limit by using different 3D convolutions.…”

Section: Related Workmentioning

confidence: 99%

Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images

Anand

Gupta

et al. 2023

Diagnostics

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Brain tumor diagnosis at an early stage can improve the chances of successful treatment and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures, such as magnetic resonance imaging (MRI), can be used to diagnose brain tumors. Deep learning, a type of artificial intelligence, can analyze MRI images in a matter of seconds, reducing the time it takes for diagnosis and potentially improving patient outcomes. Furthermore, an ensemble model can help increase the accuracy of classification by combining the strengths of multiple models and compensating for their individual weaknesses. Therefore, in this research, a weighted average ensemble deep learning model is proposed for the classification of brain tumors. For the weighted ensemble classification model, three different feature spaces are taken from the transfer learning VGG19 model, Convolution Neural Network (CNN) model without augmentation, and CNN model with augmentation. These three feature spaces are ensembled with the best combination of weights, i.e., weight1, weight2, and weight3 by using grid search. The dataset used for simulation is taken from The Cancer Genome Atlas (TCGA), having a lower-grade glioma collection with 3929 MRI images of 110 patients. The ensemble model helps reduce overfitting by combining multiple models that have learned different aspects of the data. The proposed ensemble model outperforms the three individual models for detecting brain tumors in terms of accuracy, precision, and F1-score. Therefore, the proposed model can act as a second opinion tool for radiologists to diagnose the tumor from MRI images of the brain.

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“…2, the accuracy of the proposed multi-input Unet model for segmentation of whole tumor and core lesion is 0.92 and 0.90, respectively. Here, the algorithm proposed in references [48,49,52] inputs more lesion information into the deep learning network through pre-processing operation, which can improve the ability to obtain non-linear lesion features.…”

Section: Analysis Of the Proposed Multi-input Imagesmentioning

confidence: 99%

“…Thus, it can effectively improve the computational efficiency, but the model training still needs a large number of datasets. To solve this problem, Nassar et al [52] input the narrow-band information in the integrated MRI images into the CNN model, which can obtain more accurate brain tumor anatomical information. Kayalıbay et al [53] propose an improved CNN model based on the filter algorithm, which effectively alleviates the problem of high-class imbalance by combining the feature maps of long skip connected lesions.…”

Section: Introductionmentioning

confidence: 99%

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Multi-input Unet model based on the integrated block and the aggregation connection for MRI brain tumor segmentation

Fang

Wang

2023

Biomedical Signal Processing and Control

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MRI Brain Tumor Segmentation Using Deep Learning. (Dept. E)

Cited by 9 publications

References 21 publications

Deep segmentation of the liver and the hepatic tumors from abdomen tomography images

Deep segmentation of the liver and the hepatic tumors from abdomen tomography images

Weighted Average Ensemble Deep Learning Model for Stratification of Brain Tumor in MRI Images

Multi-input Unet model based on the integrated block and the aggregation connection for MRI brain tumor segmentation

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