Optimization empowered hierarchical residual VGGNet19 network for multi-class brain tumour classification

Krishna, P. Rama; Prasad, V. Kamakshi; Battula, Tirumula Krishna

doi:10.1007/s11042-022-13994-7

Cited by 8 publications

(2 citation statements)

References 40 publications

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“…This [15] study proposed a deep learning architecture to automatically identify brain cancers using two-dimensional MRI slices with high accuracy and low error. Square Array Filtering (SAF) removes noise from the input picture and generates a square grid format array to update missing pixel values, creating a noiseless image.…”

Section: Ease Of Usementioning

confidence: 99%

Optimized Brain Tumour Segmentation and Classification Using VGG UNET and VGG-19 with ABC-WOA Algorithm

Sharma

2024

IJSREM

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Accurate brain tumour classification is essential for treatment planning and patient care in medical image analysis. In this study, we used sophisticated deep learning algorithms to improve brain tumour categorization. We used VGG-UNET for segmentation to precisely delineate tumour locations in MRI scans and VGG-19 for classification, a popular convolutional neural network architecture for image classification. We used a hybrid ABC-WOA hyperparameter tweaking technique to increase the accuracy and resilience of our VGG-19 model. We compared our model against ResNet- 50 and AlexNet, two popular convolutional neural network designs, on accuracy, precision, recall, and F1-scores. Hyperparameter-tuned VGG-19 model had excellent discrimination, with accuracy metrics topping 99.1% and a remarkable AUC value of 0.99. ResNet-50 and AlexNet performed well, however they were not as accurate and precise as VGG-19. These data show that our technique could revolutionize brain tumour classification, providing doctors with a trustworthy tool for precise diagnosis and treatment planning. Future study might examine the scalability and generalizability of our findings in larger datasets and clinical contexts, improving patient outcomes and neuro-oncology research. VGG-UNET segmentation, VGG-19 classification, and a hybrid ABC-WOA hyperparameter tuning approach improve brain tumour classification accuracy. The hyperparameter-tuned VGG-19 model's proven performance makes it a promising choice for clinical brain tumour classification tasks, providing physicians with accurate diagnosis and prognosis. Keywords— VGG-UNET, VGG-19, ResNet-50, Alexnet, Brain-Tumour, ABC-WOA, Segmentation, MRI Images.

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Section: Ease Of Usementioning

confidence: 99%

Optimized Brain Tumour Segmentation and Classification Using VGG UNET and VGG-19 with ABC-WOA Algorithm

Sharma

2024

IJSREM

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“…A brain tumor is an abnormal growth of brain cells, which is considered one of the most prevalent and deadly diseases ( 1 ). Relevant data show that brain tumors account for approximately 1.6% of the incidence and 2.5% of the mortality of all tumors, posing a great threat to human health ( 2 ).…”

Section: Introductionmentioning

confidence: 99%

CMAF-Net: a cross-modal attention fusion-based deep neural network for incomplete multi-modal brain tumor segmentation

Sun,

Ding,

et al. 2024

Quant Imaging Med Surg

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Background The information between multimodal magnetic resonance imaging (MRI) is complementary. Combining multiple modalities for brain tumor image segmentation can improve segmentation accuracy, which has great significance for disease diagnosis and treatment. However, different degrees of missing modality data often occur in clinical practice, which may lead to serious performance degradation or even failure of brain tumor segmentation methods relying on full-modality sequences to complete the segmentation task. To solve the above problems, this study aimed to design a new deep learning network for incomplete multimodal brain tumor segmentation. Methods We propose a novel cross-modal attention fusion-based deep neural network (CMAF-Net) for incomplete multimodal brain tumor segmentation, which is based on a three-dimensional (3D) U-Net architecture with encoding and decoding structure, a 3D Swin block, and a cross-modal attention fusion (CMAF) block. A convolutional encoder is initially used to extract the specific features from different modalities, and an effective 3D Swin block is constructed to model the long-range dependencies to obtain richer information for brain tumor segmentation. Then, a cross-attention based CMAF module is proposed that can deal with different missing modality situations by fusing features between different modalities to learn the shared representations of the tumor regions. Finally, the fused latent representation is decoded to obtain the final segmentation result. Additionally, channel attention module (CAM) and spatial attention module (SAM) are incorporated into the network to further improve the robustness of the model; the CAM to help focus on important feature channels, and the SAM to learn the importance of different spatial regions. Results Evaluation experiments on the widely-used BraTS 2018 and BraTS 2020 datasets demonstrated the effectiveness of the proposed CMAF-Net which achieved average Dice scores of 87.9%, 81.8%, and 64.3%, as well as Hausdorff distances of 4.21, 5.35, and 4.02 for whole tumor, tumor core, and enhancing tumor on the BraTS 2020 dataset, respectively, outperforming several state-of-the-art segmentation methods in missing modalities situations. Conclusions The experimental results show that the proposed CMAF-Net can achieve accurate brain tumor segmentation in the case of missing modalities with promising application potential.

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A systematic analysis of magnetic resonance images and deep learning methods used for diagnosis of brain tumor

Solanki¹,

Singh

Chouhan

et al. 2023

Multimed Tools Appl

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Optimization empowered hierarchical residual VGGNet19 network for multi-class brain tumour classification

Cited by 8 publications

References 40 publications

Optimized Brain Tumour Segmentation and Classification Using VGG UNET and VGG-19 with ABC-WOA Algorithm

Optimized Brain Tumour Segmentation and Classification Using VGG UNET and VGG-19 with ABC-WOA Algorithm

CMAF-Net: a cross-modal attention fusion-based deep neural network for incomplete multi-modal brain tumor segmentation

A systematic analysis of magnetic resonance images and deep learning methods used for diagnosis of brain tumor

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