K-NET+SEGAN-BASED SEGMENTATION WITH GANNET AQUILA OPTIMIZATION ALGORITHM-ENABLED DEEP MAXOUT NETWORK FOR BRAIN TUMOR CLASSIFICATION USING MRI

A Validation Framework for Brain Tumor Segmentation

BrainView: A cloud-based deep learning system for brain image segmentation, tumor detection and visualization.

Brain tumors pose a significant clinical challenge due to their aggressive nature and the complexity of accurate diagnosis. Traditional deep learning models, primarily based on Convolutional Neural Networks (CNNs), have shown promise in tumor segmentation and classification from Magnetic Resonance Imaging (MRI) scans. However, these approaches often struggle with capturing global spatial dependencies, generalizing from limited annotated data, and fine-tuning performance across varying imaging conditions. This paper presents an advanced hybrid framework that combines self-supervised learning, Vision Transformer (ViT), and Quantum Particle Swarm Optimization (QPSO) to enhance segmentation and classification of brain tumors. The methodology begins with preprocessing involving skull stripping, contrast enhancement, and denoising using a Cycle-Consistent Generative Adversarial Network (CycleGAN). Feature representation is learned through a Simple Framework for Contrastive Learning of Visual Representations (SimCLR), followed by segmentation using a DeepLabv3+ architecture with a ViT encoder. Tumor classification is handled by a lightweight ViT model, whose hyperparameters are optimized using QPSO. Evaluation on the Brain Tumor Segmentation (BraTS) 2021 dataset demonstrates a Dice Similarity Coefficient (DSC) of 95.4% and classification accuracy of 96.7%, confirming the robustness and superiority of the proposed approach over conventional methods.

Brain tumor segmentation and classification is really a difficult process to identify and detect the tumor region. Magnetic resonance image (MRI) gives valuable information to find the affected area in the brain. The MRI brain image is initially considered, which specifies four various modalities of the brain such as T1, T2, T1C, and the Flair. The preprocessing methodologies and the state-of-the-art MRI-related brain tumor segmentation and classification methods are discussed. This study describes the different types of brain tumor segmentation and classification techniques with its most important contributions. The survey of brain tumor segmentation and classification (BTSC) technique including the four main phases—preprocessing, feature extraction, segmentation, and classification—is discussed. The different types of BTSC techniques are listed, along with their great contributions. A review of recent articles on classifiers shows the eccentric features of classifiers for research.

Enhanced performance of Dark-Nets for brain tumor classification and segmentation using colormap-based superpixel techniques

Automated segmentation and classification of brain tumor is important to avoid misdiagnosis and to improve chances of patients' survival. In this paper, we present a fully automated technique for segmentation and classification of brain tumor into three different regions namely Complete Tumor, Tumor Core and Enhancing Tumor. We use a cascaded Random Decision Forest (RDF) model for classification. In our experiments, we use BRATS 2013 3D MR images dataset which contains T1, T1c, T2 and Flair MRI sequences. These sequences are standard in clinical acquisition. Using 10-fold cross validation for evaluation, we achieve promising Dice scores of 0.90, 0.79 and 0.84 for Complete Tumor, Tumor Core and Enhancing Tumor, respectively.

Brain tumor segmentation and classification (BTSC) from magnetic resonance imaging (MRI) is an important procedure in medical predictions, improving the precision and speed of treatment plans. However, the automatic detection of tumors is challenging because of similar intensity patterns, indistinct boundaries, and varying tumor shapes. This paper suggests a novel deep transfer learning (DTL) methodology to accurately segment and detect brain tumors (BTs) from MRI. Initially, the denoising is carried out using improved anisotropic diffusion filtering (IADF), and then dataset diversity is enhanced by applying data augmentation. Afterwards, a Swin Transformer U-shaped network with dense connections (STUNetDS) is utilized for segmenting the tumor, and finally, Kullback–Leibler loss with Optimized InceptionResNetv2 (KLOINRNetv2) is used for the classification of the tumor. The system uses the Multi-Strategy Improved Kookaburra Optimization (MSIKBO) algorithm for tuning the network parameters. The model achieves 98.67% accuracy for detecting and classifying BTs from the Kaggle BraTS2019 dataset, higher compared to the baseline techniques. The reliable and scalable solutions are offered by the proposed model, making it more suitable for automated tumor assessment in clinical applications.

Brain tumor detection and segmentation from Magnetic Resonance Imaging (MRI) images is being one of the emerging fields in the biomedicine. A formidable undertaking in brain tumor surgery, medical care, treatment programme and quantitative assessment of MRI images is to precisely diagnose its location and extent. Recently, the convolutional neural network (CNN) based detection and segmentation method on brain tumor MRI images is being one of the emerging fields in the medical imaging as an automatic clinic treatment and evaluation solution. In this article, we put forward a brand new quadruplet loss in CNN framework, which achieves higher accuracy in brain tumor detection and segmentation than other pairwise loss and triplet loss methods. By applying the proposed quadruplet loss to the original L2Net CNN architecture leads to a more compact descriptor named QuadrupletNet. From our experiments, QuadrupletNet shows higher performance than other state-of-the-art loss functions e.g., the Triplet loss, as indicated in experiments on Multimodal Brain Tumor Image Segmentation (BRATS 2018) datasets, and on our own collected MRI brain tumor datasets (named MBTD).

The brain is considered a main important part of human body as it participate to control all the activities of human. Under some unhealthy conditions the abnormal and uncontrolled growth of tissue may take place which is unnatural process. This kind of process taking place in human brain is referred as “brain tumor”. The brain tumor may be of cancerous (malignant) or may be of non-cancerous (Benign) and its detection is a very much important to reduced the death rate of humans suffering from tumor. The detection of brain tumor can be performed by using various image processing techniques like brain magnetic resonance imaging (MRI), Computed Tomography (CT), Electroencephalography (EEG), Positron Emission Tomography (PET), Magneto Encephalography (MEG) etc. Among these techniques the brain MRI is widely adopted in the world due to its significant features. But to detect tumor in the brain MRI, the processes like preprocessing, segmentation and classification are must. This paper deals with the various aspects of the brain tumor detection. The paper discusses the significant researches which are meant for brain tumor detection through MRI quality enhancement, MRI segmentation and MRI classification (as cancerous or not). The outcome of the paper discussed that there were no proper researches are performed to detect the brain tumor, focused on good quality of image, less researches on segmentation and classification of brain tumor. Finally the paper gives the future research scope to overcome the addressed research gap.

Segmentation and classification of brain tumors from MRI images based on adaptive mechanisms and ELDP feature descriptor

Manual diagnosis of tumors on magnetic resonance images (MRIs) entails more time and doing so increases the risk of human error and incorrect tumor type identification and classification. Cells develop quickly and uncontrollably, which causes brain tumors and may cause death unless handled in the beginning stages. Therefore, a transfer learning framework for brain tumor classification is presented to simplify the task of healthcare professionals by automating tough medical processes. MRI image analysis was done on a publicly available dataset from Kaggle. The proposed method is implemented on VGG16, ResNet50, EfficientNetB0, and U-Net architectures. Training accuracy and test accuracy of all these four neural architectures compared and results that U-Net performs better in the classification of brain tumors compared to the other networks.

HSA-net with a novel CAD pipeline boosts both clinical brain tumor MR image classification and segmentation

A new clinical diagnosis system for detecting brain tumor using integrated ResNet_Stacking with XGBoost

Brain tumor is a type of cancer which causes abnormal cells growth in the brain. It can be cured if we detect the brain tumor at an early stage. Brain tumor patients often suffer from blood clot, movement control loss, vision loss, behavioral changes, hormone changes, etc. The location, type, and size of the tumor have an effect on the normal functioning of the individual. To examine the size, shape, and location of tumor in the brain, Magnetic Resonance Imaging (MRI) is used. MRI image produces a clear anatomical view of brain and any small abnormality of brain is perceptible by MRI image. Brain tumor classification, detection, and segmentation are huge concerns of researchers. Artificial Intelligence (AI)-based methods can be used to detect brain tumor. Among various deep learning-based algorithms CNN acquired a better position in image classification. CNN classification accuracy depends on some network parameters as convolutional filters, rectification functions, polling functions, and iteration numbers, etc. It is a problem to determine effective values of the convolution filter size, number, and convolution stride for better accuracy in classification. CNN has the capabilities to produce and learn effective features on large datasets. In this paper, we are using convolutional neural network model which takes the feature maps preprocessed to classify the MRI brain image datasets. This paper uses deep learning which processes NIFTI images and creates 3D convolutional neural network, which can be trained to detect and segment structures if corresponding ground truth labels are provided for training.

In brain tumor diagnosis, clinicians integrate their medical knowledge and brain magnetic resonance imaging (MRI) scans to obtain the nature and pathological characteristics of brain tumors and to decide on treatment options. However, manually detecting and segmenting brain tumors in today's brain MRI, where a large number of MRI scans taken for each patient, is tedious and subjected to inter and intra observer detection and segmentation variability. As result a number of methods have been proposed in recent years to fill this gap, but still there is no commonly accepted automated technique by clinicians to be used in clinical floor due to accuracy and robustness issues. In our approach, an automatic brain tumor detection and segmentation framework that consists of techniques from skull stripping to detection and segmentation of brain tumors is proposed with fuzzy Hopfield neural network as its final tumor segmentation technique. Through preprocessing, image fusion and initial tumorous slice classification, the final hybrid intelligent fuzzy Hopfield neural network algorithm based tumor segmentation, and tumor region detection and extraction is achieved. The performance of the proposed framework is evaluated on various MR images including simulated and real, normal and tumorous. Quantitatively the method is validated against available ground truth using commonly used validation metrics. The final segmentation mean and standard deviation result in Jaccard similarity index, Dice similarity score, sensitivity and specificity are 0.8569+/-0.0896, 0.9186+/-0.0638, 0.9480+/-0.0402 and 0.9917+/-0.0387 respectively. Quantitative and qualitative segmentation result indicates the potential of the proposed framework.