"Brain Tumour Image Segmentation Using Deep Networks" focuses on automated segmentation techniques for brain tumors using MRI images. The paper compares deep learning algorithms with traditional computer vision methods, highlighting the superior performance of deep learning in semantic segmentation challenges. It utilizes two specific deep learning models: 3-Dimensional Space Invariant Artificial Neural Networks and U-Net, which were trained on the BraTs dataset. The proposed models achieved impressive roll ratings for detecting cancerous cells, whole tumors, and tumor layers, indicating significant advancements in tumor segmentation accuracy compared to existing designs.

"Causality-Driven Graph Neural Network for Early Diagnosis of Pancreatic Cancer in Non-Contrast Computerized Tomography" addresses the challenge of early pancreatic cancer diagnosis using non-contrast CT images. The paper introduces a causality-driven graph neural network (GNN) to enhance the stability and generalization of early diagnosis. It develops a multiple-instance learning framework to extract fine-grained features of pancreatic tumors and implements a causal contrastive mechanism to separate causal and non-causal components of the features. The effectiveness of the proposed method is validated through extensive experiments on multi-center datasets, demonstrating its potential as a valuable tool for early diagnosis of pancreatic cancer.

This paper introduces a novel approach to brain tumor segmentation using Graph Neural Networks (GNNs) that incorporate both spectral and spatial insights. It evaluates various supervoxel creation methods and represents 3D MRI images as a graph structure. The study highlights the innovative combination of Spatial and Spectral GNNs to capture both local and global information, enhancing the model's ability to classify brain tumors into distinct types. The results demonstrated significant improvements in segmentation accuracy for various tumor sub-regions compared to traditional methods, underscoring the potential of the Spectral–Spatial GNN model to enhance brain tumor segmentation accuracy, precision, and computational efficiency.

"An early detection and segmentation of Brain Tumor using Deep Neural Network" presents a two-step approach to address challenges in brain tumor segmentation. The first step involves a preprocessing method that narrows the focus to a small region of interest within each MRI slice, reducing computation time and mitigating overfitting. The second step features a Cascade Convolutional Neural Network (C-CNN) designed to extract both local and global features through separate routes. The paper also introduces a novel Distance-Wise Attention (DWA) mechanism to improve segmentation accuracy by considering spatial relationships between the tumor center and surrounding brain tissue. The proposed method was validated against the BRATS 2018 dataset, achieving impressive mean dice scores for whole tumors, enhancing tumors, and tumor cores.

This paper discusses the application of deep learning techniques, particularly U-Net models, for brain tumor segmentation in MRI images. It highlights the use of the U-Net architecture, known for its effectiveness in biomedical image segmentation, and introduces an Attention mechanism to enhance the model's ability to focus on relevant features. The study utilizes the BraTS2021 dataset and employs various data augmentation techniques. The results show high accuracy rates for tumor enhancement areas, core areas, and overall areas, demonstrating the effectiveness of the proposed method in MRI brain tumor image segmentation.

The paper "Multi-class Brain Tumor Segmentation using Graph Attention Network" addresses the need for effective brain tumor segmentation from MRI scans. It utilizes Graph Neural Networks (GNNs), specifically a Graph Attention Network (GAT), to enhance the segmentation process. The authors represent multiparametric MRI data as a region adjacency graph, allowing the model to learn from relationships between different brain regions. The proposed model is evaluated on the BraTS2021 validation dataset, showing competitive mean dice scores and Hausdorff distances, and outperforming a GNN-based baseline by significant margins.

"Enhanced brain tumor classification using graph convolutional neural network architecture" presents a novel approach to brain tumor detection and classification using advanced neural network techniques. The paper introduces a Graph Convolutional Neural Network (GCNN) model, combining a 26-layer CNN with GNN techniques. This integration allows for the consideration of local neighborhood information, enhancing the model's ability to classify tumor types accurately. The proposed model, particularly the Net-2 architecture, achieved a high accuracy of 95.01%, demonstrating significant improvement over existing models in similar classification tasks.

This paper explores the advancements in brain tumor diagnosis and segmentation through deep learning techniques. It highlights the use of pre-trained convolutional neural networks (CNNs) for identifying brain tumors in medical images. The study details the model-building process, which involved training a deep CNN using the BraTS dataset. The model employed a categorical cross-entropy loss function and the Adam optimizer. The results were promising, with the model achieving a validation accuracy of 98% in identifying and segmenting brain tumors.

"Enhancing Glioma Brain Tumor Detection from MRI using Deep Learning Techniques" presents a comprehensive approach to improving brain tumor detection. The study introduces a hybrid model that integrates deep learning, machine learning, and traditional image processing techniques. The methodology involves image enhancement using histogram equalization, feature extraction using the grey level co-occurrence matrix, classification using convolutional neural networks (CNNs), and segmentation using a morphological segmentation algorithm. The results indicate that the hybrid model outperforms existing methods in accuracy, sensitivity, and specificity metrics.

The paper "Improved Brain Tumor Segmentation in MR Images with a Modified U-Net" presents advancements in brain tumor segmentation using deep learning techniques. It focuses on modifications to the U-Net architecture, including changes in kernel size, the number of channels, dropout ratios, and activation functions. The study reports impressive results, achieving a Global Accuracy of 99.4% and a Dice similarity coefficient of 90.2%. The model was trained efficiently on a large dataset of magnetic resonance images, with potential for integration into medical care and hospital protocols to assist radiologists in identifying tumor locations and suspicious regions.

"Automatic Brain Tumor Segmentation from MRI Scans using U-net Deep Learning" explores advancements in brain tumor segmentation using deep learning techniques. The study discusses the importance of preprocessing, specifically mentioning Edge Enhancing Diffusion (EED) filtering. It builds upon the U-net architecture, incorporating a redesigned decoder path with attention mechanisms. The integration of channel and spatial attention mechanisms is highlighted as a way to capture long-range dependencies and reduce background noise. The study uses the BRATS 2020 challenge dataset for evaluation, reporting impressive average Dice Similarity Coefficient (DSC) scores.

This paper proposes a Fully Automatic Heterogeneous Segmentation using Support Vector Machine (FAHS-SVM) for brain tumor segmentation based on deep learning techniques. It describes the pre-processing steps of skull stripping and morphological operation, as well as the feature extraction process, which includes textural features. The paper presents experimental results of the proposed FAHS-SVM method, including the Dice similarity coefficient, probability of detection ratio evaluation, and classification error. The results show that the proposed method achieves high accuracy and outperforms other existing methods in terms of segmentation accuracy and classification error.

The paper "Enhancing Glioma Brain Tumor Detection from MRI using Deep Learning Techniques" presents a comprehensive approach to improving brain tumor detection. It introduces a hybrid model that integrates deep learning, machine learning, and traditional image processing techniques. The methodology involves image enhancement, feature extraction, classification using convolutional neural networks, and segmentation. The paper presents performance evaluations using various criteria, including accuracy, sensitivity, and specificity, demonstrating that the hybrid model outperforms existing methods in these metrics.

This literature review synthesizes current research findings in deep learning for brain tumor segmentation. It discusses advances in techniques such as Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs), highlighting their superior performance over traditional methods. The review addresses challenges such as data scarcity and explores various learning approaches and data augmentation techniques. It also discusses innovations in segmentation approaches, including multi-scale processing techniques and the integration of Conditional Random Fields with Fully Convolutional Neural Networks. The review identifies knowledge gaps and suggests future research directions, emphasizing the need for extensive validation and the development of novel architectures tailored for brain tumor segmentation.

This paper proposes a Fully Automatic Heterogeneous Segmentation using Support Vector Machine (FAHS-SVM) for brain tumor segmentation. It discusses the importance of brain tumor detection and segmentation, and reviews related research including machine learning-based methods and image processing techniques. The paper describes the proposed FAHS-SVM method in detail, including pre-processing steps, segmentation, and feature extraction. It presents experimental results showing that the proposed method achieves high accuracy and outperforms other existing methods in terms of segmentation accuracy and classification error.

This paper provides a literature survey on deep learning methods for brain tumor segmentation in medical images. It discusses the challenges of manual segmentation and the need for automated methods. The authors review both traditional machine learning and deep learning methods, with a focus on convolutional neural networks (CNNs). They highlight state-of-the-art deep learning techniques for brain tumor segmentation and discuss unique challenges and their possible solutions. The paper also mentions the use of ensembles and U-Net based models for improving segmentation performance.

This research proposes BrainGNN, a graph neural network framework for analyzing functional magnetic resonance images (fMRI) and discovering neurological biomarkers. BrainGNN contains novel ROI-aware graph convolutional layers and ROI-selection pooling layers, providing transparency in medical image analysis. The framework outperforms alternative fMRI image analysis methods on two independent datasets: an Autism Spectrum Disorder (ASD) fMRI dataset and data from the Human Connectome Project (HCP) 900 Subject Release. The obtained community clustering and salient ROI detection results show high correspondence.

This research proposes a brain tumor segmentation method using Convolutional Neural Networks (CNN) in Magnetic Resonance Imaging (MRI) images. The study demonstrates the effectiveness and efficiency of the proposed method through experimental examinations using benchmark data. It introduces the rifleman technique, a statistical region merging approach, to improve comprehension of both normal brain activity and abnormalities associated with psychiatric diseases. The researchers suggest future work could expand this method to advanced 3D and 4D images and use it for forecasting cell states in brain imaging.

This research develops a novel methodology for categorizing brain tumors using a combination of image enhancement techniques and a proposed convolutional neural network. The methodology achieved a remarkable accuracy of 97.84%, demonstrating its robust capacity for generalization. The study aims to revolutionize medical imaging methods by creating real-time brain tumor detection systems and establishing three-dimensional networks to analyze other medical images. The methodology was tested on a publicly accessible MRI dataset and employed various image enhancement techniques. The study also compared the proposed methodology with pre-trained models, showing that it outperformed them in terms of accuracy, precision, recall, and F1-score.

This research proposes a brain tumor segmentation method using a preprocessing approach, a Cascade Convolutional Neural Network (C-CNN), and a Distance-Wise Attention (DWA) mechanism. The preprocessing approach focuses on a limited area of the image to extract key features, reducing unnecessary uninformative parts. The C-CNN model mines both local and global features in two different routes, and the DWA mechanism considers the effect of the brain tumor location in four input modalities. The proposed method achieves competitive results on the BRATS 2018 dataset, with high mean dice scores for whole tumor, enhancing tumor, and tumor core.

This research introduces a novel Spectral-Spatial Graph Neural Network (GNN) model for brain tumor segmentation in MRI images. The study demonstrates that the proposed model outperforms traditional segmentation methods and individual Spatial or Spectral GNNs in terms of accuracy and precision. The Spectral-Spatial GNN model shows significant improvements in both whole tumor and sub-region segmentation, as evidenced by the dice coefficient and other metrics. The findings have the potential to enhance brain tumor diagnosis, treatment planning, and fundamental research.

This research proposes a hybrid approach called HCNN-RF for tumor detection in MRI images using a combination of Convolutional Neural Networks (CNN) and Random Forest (RF) algorithms. The method was evaluated using the BRATS 2019 dataset and achieved significant improvements in accuracy, sensitivity, and F1 score compared to alternative methods. The study also suggests potential future works, including the use of deeper neural networks, new pre-processing methods, and expanding the method to detect tumors in other parts of the body.

This research develops a fine-tuned YOLOv7 model for brain tumor detection in MRI images, which achieved high precision and recall. The proposed model outperformed other CNN architectures in terms of validation accuracy. The study also demonstrated the importance of transfer learning and fine-tuning in enhancing the precision of brain tumor diagnosis and the unique capacities of each CNN design.

This paper introduces a custom-designed CNN architecture optimized for detecting brain tumors in MRI scans. The literature survey highlights the evolution of AI techniques in this field, from traditional machine learning methods to advanced CNN models. It discusses challenges such as data scarcity and the need for specialized approaches due to the complexities of brain anatomy. The proposed method aims to enhance accuracy and efficiency by building on foundational research while integrating state-of-the-art feature extraction and deep learning optimization strategies.

This paper presents an improved Unet model for brain tumor segmentation. The authors introduce several enhancements to the traditional Unet architecture, including a modified downsampling approach, a Multiple-Attention based Feature Pyramid Network (MA-FPN), and an Edge Extraction Module. The paper compares the proposed model with existing segmentation models such as Unet, PSPNet, ICNet, and Fast-SCNN. Experimental results demonstrate that the improved Unet model outperforms traditional models in terms of segmentation accuracy, addressing the critical need for accurate and efficient brain tumor segmentation in medical imaging.(2nd 25 set)

Jahir Pasha et al. [1] explore deep learning for brain tumor segmentation using 3D Space Invariant Artificial Neural Networks and U-Net. The study emphasizes automated segmentation's importance for heterogeneous tumor analysis. Comparing deep learning with traditional computer vision methods, the research highlights deep learning's superior performance. Trained on the BraTS dataset, the models achieved promising roll ratings (0.768, 0.915, 0.820) for different tumor components. This indicates advancements in segmentation accuracy. Future research directions include model refinement and exploring other datasets. The work showcases deep learning's potential in medical imaging. The study utilizes MRI images for analysis. The authors emphasize the need for precise segmentation. The paper highlights the evolution of image segmentation techniques.

Xinyue Li et al. [2] addressing the lack of effective screening methods, the study uses non-contrast CT images. A multiple-instance learning framework extracts fine-grained tumor features. The causal contrastive mechanism enhances model stability and generalization. Validation on multi-center datasets demonstrates clinical significance. The research highlights the need for improved diagnostic methods. The proposed approach leverages advanced machine learning. The GNN models complex data relationships. This allows for more accurate and stable diagnosis.

Sina Mohammadi et al. [3] addressing limitations of traditional methods, the model incorporates both spatial and spectral information. Supervoxel creation techniques are evaluated for optimal graph representation. 3D MRI images are converted into graph structures for analysis. Combining Spatial and Spectral GNNs captures local and global tumor characteristics. Performance metrics demonstrate significant improvements in accuracy. The spectral GNN leverages the Laplacian matrix. The model classifies tumor sub-regions (necrosis, edema). The research highlights GNNs’ potential in medical imaging. Future work may explore other applications of GNNs.

Xue et al. [4] provides a comprehensive overview of recent advancements in tumor segmentation using deep learning (DL) techniques. It highlights the importance of multi-modal imaging, which offers a more holistic view of tumors, enhancing diagnosis and treatment planning. The review emphasizes DL’s significant role in improving segmentation accuracy and its ability to process complex data. The authors conducted a systematic review of 78 articles published between 2018 and 2023, focusing on multi-modal tumor segmentation. Future directions suggest further exploration of DL techniques and multi-modal data fusion for better accuracy.

Mukul Aggarwal et al. [5] highlights existing research on brain tumor segmentation. Challenges related to tumor variability are addressed. The Improved ResNet addresses gradient diffusion issues. DSC is used as a performance metric. Comparative analysis shows significant improvement over CNN and FCN. The improved ResNet enhances information flow. This results in improved accuracy, recall, and F-measure. The study contributes to advancements in the field.

Ramin et al. [6] focuses on brain tumor segmentation from multi-modal MRI. The BraTS2021 dataset is used for training and evaluation. Preprocessing includes random clipping and flipping. The Attention mechanism improves feature selection. High accuracy rates are achieved for different tumor areas. A low loss function value indicates effective learning. The method is highlighted for its simplicity and efficiency. This makes it suitable for clinical application. The study contributes to deep learning in medical imaging.

Dhurmil Patel et al. [7] tries to address Manual segmentation limitations. The study leverages advancements in mpMRI technology. GNNs are introduced for modeling spatial relationships. mpMRI data is represented as a region adjacency graph. The model is evaluated on the BraTS2021 dataset. Competitive dice scores and Hausdorff distances are achieved. The GAT outperforms GNN baselines. The research highlights the potential of GATs in medical imaging. This approach combines structural and GNN capabilities.

Ravinder et al. [8] emphasize on Early detection for accurate diagnosis. Limitations of conventional models are addressed. GNNs are introduced for processing graph-structured data. The model combines a 26-layer CNN with GNN techniques. Local neighbourhood information enhances classification. Net-2 architecture achieves high accuracy (95.01%). The study addresses gaps in existing literature. The model outperforms many existing models. The research utilizes advanced neural network techniques.

Mostafa et al. [9] address challenges in brain tumor diagnosis. Deep learning is presented as a solution. The use of pre-trained CNNs is highlighted. A dedicated brain tumor dataset is created. The model is trained using the BraTS dataset. Categorical cross-entropy loss and Adam optimizer are used. The model achieves a validation accuracy of 98%. Deep learning enhances efficiency and accuracy. The study contributes to advancements in the field.

Sankara Narayanan et al. [10] integrates deep learning, machine learning, and image processing. Histogram equalization enhances image quality. Grey level co-occurrence matrix extracts features. CNNs classify images as normal or abnormal. Morphological segmentation isolates the tumor. Performance evaluation uses accuracy, sensitivity, and specificity. The hybrid model outperforms existing methods. The study highlights automated detection's significance. The research contributes to MRI image analysis. The method shows potential for clinical use.

Hiam Alquran et al. [11] emphasizes deep learning's role in medical imaging. Modifications to U-Net's kernel size, channels, and activation function are detailed. Leaky ReLU replaces ReLU for improved performance. High global accuracy (99.4%) and Dice coefficient (90.2%) are achieved. Training efficiency is demonstrated with a short training time. The approach has potential for clinical integration. The modifications significantly enhance performance. The research contributes to improving diagnosis. The paper highlights the importance of brain tumor detection.

Omar Alirr et al. [12] Segmentation's importance for diagnosis and treatment is highlighted. The role of CNNs in medical image segmentation is emphasized. EED filtering enhances tumor visibility in MRI scans. The decoder path incorporates attention mechanisms. Channel and spatial attention improves performance. The model is evaluated on the BRATS 2020 dataset. High DSC scores (90.80%) are reported. The method accurately segments brain tumors. The study advances automated segmentation techniques.

Duman et al. [13] focused on Brain tumor segmentation is a crucial task for effective radiotherapy planning, and recent advancements in deep learning (DL) have significantly enhanced image segmentation in medical imaging. In particular, 2D and 2.5D U-NET architectures have been employed to improve segmentation accuracy. The 2.5D U-NET model, which uses three slices of MRI data as input, offers better context and feature extraction compared to traditional 2D models. Studies show that the 2.5D U-NET achieved superior performance, with Dice scores up to 94.42% on the BraTS 2021 dataset. However, challenges remain when applied to local datasets.

Umarani et al. [14] CNNs and GANs are discussed as key architectures. The BRATS benchmark is highlighted for algorithm evaluation. Challenges like data scarcity are addressed. Supervised, unsupervised, and semi-supervised learning are explored. Data augmentation techniques are discussed. The integration of advanced imaging modalities is considered. Future research directions are identified. The review synthesizes current research findings. The conclusion emphasizes deep learning's potential.

Zheshu Jia et al. [15] discusses challenges with manual segmentation methods. It reviews existing machine learning and image processing techniques. FAHS-SVM includes skull stripping and morphological operations. Textural features like mean, standard deviation, and entropy are extracted. SSIM, MSE, and PSNR are used as quality metrics. The method achieves high accuracy in segmentation and classification. The results are compared with existing methods. Potential applications and future directions are discussed. The study contributes to automated brain tumor segmentation.

Magadza et al. [16] worked on Challenges of manual segmentation and the need for automated solutions are highlighted. Traditional machine learning and deep learning methods are reviewed. CNNs are emphasized for their performance in computer vision. Challenges in medical image analysis are discussed. State-of-the-art techniques are highlighted. Ensembles and U-Net models are mentioned. Advancements and future research potential are summarized. The survey provides a comprehensive overview. The focus is on deep learning applications in medical imaging.

Xiaoxiao Li et al. [17] aims to discover neurological biomarkers. BrainGNN uses ROI-aware graph convolutional layers. ROI-selection pooling layers provide transparency. The framework outperforms alternative fMRI analysis methods. Evaluation metrics are used on ASD and HCP datasets. Community clustering results are highly correspondent. Salient ROI detection is also achieved. The framework offers interpretable fMRI analysis. The research contributes to neurological biomarker discovery.

Madhumitha R et al. [18] validated method on benchmark data. The rifleman technique for image segmentation is introduced. It improves understanding of normal and abnormal brain activity. The method is effective and efficient. Future work includes expanding to 3D and 4D images. Feature extraction and data reduction are discussed. This allows faster processing of data mining algorithms. The method can be used for predicting cell states. The research contributes to brain image analysis. The study utilizes MRI images.

Zahid Rasheed et al. [19] used Image enhancement techniques and a CNN are combined. The method achieves high accuracy (97.84%). Gaussian-blur sharpening and CLAHE are used. The methodology is tested on a Kaggle MRI dataset. Concatenation concepts within convolutional layers improve performance. Comparisons with pre-trained models (VGG16, ResNet50) show superior results. Limitations include the need for comprehensive evaluation. Future work involves exploring different imaging modalities. The study demonstrates the model's robust capacity.

Ranjbarzadeh et al. [20] focuses preprocessing approach on a limited image area. This reduces computation and mitigates overfitting. A Cascade CNN extracts local and global features. A Distance-Wise Attention mechanism considers tumor location. The method is evaluated on the BRATS 2018 dataset. Competitive dice scores are achieved for whole tumor and core regions. The approach uses four input modalities. The results demonstrate the method's effectiveness. The research contributes to deep learning in medical imaging. The study introduces a novel approach to segmentation.
21.Ayesha et al. [21] underscores the importance of accurate brain tumor classification for effective treatment, highlighting the challenges posed by tumor shape variability in MRI scans. It reviews deep learning models like VGG16, ResNet, and EfficientNet, with EfficientNet outperforming the others in terms of accuracy, sensitivity, and specificity. This model's ability to extract complex features from MRI data is crucial for precise classification. The study suggests that advanced deep learning techniques can significantly enhance diagnostic reliability, improving healthcare outcomes.

Mahdi Mir et al. [22] combines CNNs and Random Forest algorithms. The method is evaluated on the BRATS 2019 dataset. Significant improvements in accuracy, sensitivity, and F1 score are achieved. The study suggests using deeper neural networks in future work. New preprocessing methods are also suggested for exploration. Expanding the method to other tumor types is proposed. The method effectively detects and isolates brain tumors. The research utilizes MRI images for analysis. The study contributes to medical image analysis. The results show promise for clinical applications.

Abdusalomov et al. [23] achieved high precision (99.5%) and recall (99.3%). It outperforms other CNN architectures like Xception and ResNet50. Transfer learning and fine-tuning enhance diagnostic precision. The study highlights the unique capacities of different CNNs. The model is evaluated on MRI images. The research contributes to brain tumor detection. The results show the effectiveness of the proposed model. The study leverages deep learning approaches. The fine-tuned YOLOv7 model shows promising results.

Eid Albalawi et al. [24] highlights the evolution of AI in brain tumor diagnosis. Traditional methods like SVMs and Random Forests are discussed. Early CNN models and their limitations are addressed. Transfer learning is highlighted as a solution for data scarcity. Advanced techniques like attention mechanisms and 3D CNNs are mentioned. The paper proposes a custom CNN architecture. The model aims to enhance accuracy and efficiency. The research builds upon foundational research. The study focuses on brain MRI analysis.

Si Can Liu et al. [25] emphasizes Accurate segmentation for diagnosis and treatment. The paper compares the improved U-Net with existing models. Downsampling, MA-FPN, and an edge extraction module are introduced. MA-FPN enhances multi-scale feature extraction. The edge extraction module improves edge learning. The improved Unet outperforms traditional models. Experimental results show significant improvements. The study highlights limitations of existing models. The research contributes to brain tumor segmentation techniques. The model efficiently extracts features while minimizing parameters. compare the first 25 and seocnd 25 papers these are the 25 papers with author names included but slighlt trimmed from above para and some papers replaced . If paper contents are  same keep the text as above which generated by you with author et al,. and if content changed then include the content in the content  with author et al. in text file without summarizing or changing the contents 15 lines for each paper