Brain Tumor Segmentation Using Deep Learning on MRI Images

Mostafa, Almetwally M.; Zakariah, Mohammed; Aldakheel, Eman Abdullah

doi:10.3390/diagnostics13091562

Cited by 11 publications

(2 citation statements)

References 46 publications

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“…REMBRANDT dataset [38] contains MRI multi sequence images of 130 patients with glioma types of grade II, grade III and grade IV. Different types of tumor types with publicly available dataset is given in Table V [33], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53].…”

Section: Publicly Available Datasetsmentioning

confidence: 99%

Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance Images

Preetha,

Priyadarsini,

Nisha

2023

IEEE Access

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The state-of-the-art works for the segmentation of brain tumor using the images acquired by Magnetic Resonance Imaging (MRI) with their performances are analyzed in this comparative study. First, the architectures of convolutional neural networks (CNN) and the variants of U-shaped Network (U-Net), a kind of Deep Neural Network (DNN) are compared and their differences are highlighted. The publicly available datasets of MRI images specifically Brain Tumor Segmentation (BraTS) are also discussed. Next, the performances of tumor segmentation of various methods in the literature are compared using the parameters such as Dice score and Hausdroff distance ( 95). This study concludes that the U-Net based architectures using the BraTS-2019 dataset outperform well compared with other CNN based architectures.

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Section: Publicly Available Datasetsmentioning

confidence: 99%

Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance Images

Preetha,

Priyadarsini,

Nisha

2023

IEEE Access

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“…Medical image segmentation is essential in disease diagnosis and treatment planning (Hesamian et al 2019). A convolution neural network (CNN)-based (Wang et al 2018) U-shaped network (U-Net) (Ronneberger et al 2015) auto-encoder and its skip-connection architecture have achieved superior results in a wide variety of medical image segmentation tasks, including liver segmentation from computed tomography (CT), vessel segmentation, tumor segmentation (Saueressig et al 2022, Mostafa andZakariah 2023), and cardiac segmentation from MRI (Isensee et al 2019). However, a prolonged challenge in U-Net-based methods is the difficulty of modeling long-range dependency as the convolution operation of CNN has intrinsic locality.…”

Section: Introductionmentioning

confidence: 99%

MSCT-UNET: multi-scale contrastive transformer within U-shaped network for medical image segmentation

Xi,

Dong,

Sheng

et al. 2023

Phys. Med. Biol.

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Objective. Automatic mutli-organ segmentation from anotomical images is essential in disease diagnosis and treatment planning. The U-shaped neural network with encoder–decoder has achieved great success in various segmentation tasks. However, a pure convolutional neural network (CNN) is not suitable for modeling long-range relations due to limited receptive fields, and a pure transformer is not good at capturing pixel-level features. Approach. We propose a new hybrid network named MSCT-UNET which fuses CNN features with transformer features at multi-scale and introduces multi-task contrastive learning to improve the segmentation performance. Specifically, the multi-scale low-level features extracted from CNN are further encoded through several transformers to build hierarchical global contexts. Then the cross fusion block fuses the low-level and high-level features in different directions. The deep-fused features are flowed back to the CNN and transformer branch for the next scale fusion. We introduce multi-task contrastive learning including a self-supervised global contrast learning and a supervised local contrast learning into MSCT-UNET. We also make the decoder stronger by using a transformer to better restore the segmentation map. Results. Evaluation results on ACDC, Synapase and BraTS datasets demonstrate the improved performance over other methods compared. Ablation study results prove the effectiveness of our major innovations. Significance. The hybrid encoder of MSCT-UNET can capture multi-scale long-range dependencies and fine-grained detail features at the same time. The cross fusion block can fuse these features deeply. The multi-task contrastive learning of MSCT-UNET can strengthen the representation ability of the encoder and jointly optimize the networks. The source code is publicly available at: https://github.com/msctunet/MSCT_UNET.git.

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SSO-CCNN: A Correlation-Based Optimized Deep CNN for Brain Tumor Classification Using Sampled PGGAN

Sahoo,

Mishra,

Brahma

et al. 2024

Int J Comput Intell Syst

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Recently, new advancements in technologies have promoted the classification of brain tumors at the early stages to reduce mortality and disease severity. Hence, there is a need for an automatic classification model to automatically segment and classify the tumor regions, which supports researchers and medical practitioners without the need for any expert knowledge. Thus, this research proposes a novel framework called the scatter sharp optimization-based correlation-driven deep CNN model (SSO-CCNN) for classifying brain tumors. The implication of this research is based on the growth of the optimized correlation-enabled deep model, which classifies the tumors using the optimized segments acquired through the developed sampled progressively growing generative adversarial networks (sampled PGGANs). The hyperparameter training is initiated through the designed SSO optimization that is developed by combining the features of the global and local searching phase of flower pollination optimization as well as the adaptive automatic solution convergence of sunflower optimization for precise consequences. The recorded accuracy, sensitivity, and specificity of the SSO-CCNN classification scheme are 97.41%, 97.89%, and 96.93%, respectively, using the brain tumor dataset. In addition, the execution latency was found to be 1.6 s. Thus, the proposed framework can be beneficial to medical experts in tracking and assessing symptoms of brain tumors reliably.

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Brain Tumor Segmentation Using Deep Learning on MRI Images

Cited by 11 publications

References 46 publications

Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance Images

Comparative Study on Architecture of Deep Neural Networks for Segmentation of Brain Tumor using Magnetic Resonance Images

MSCT-UNET: multi-scale contrastive transformer within U-shaped network for medical image segmentation

SSO-CCNN: A Correlation-Based Optimized Deep CNN for Brain Tumor Classification Using Sampled PGGAN

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