Brain Tumor Classification Research Articles

A Heuristic Strategy Assisted Deep Learning Models for Brain Tumor Classification and Abnormality Segmentation

ABSTRACTBrain tumors are prevalent forms of malignant neoplasms that, depending on their type, location, and grade, can significantly reduce life expectancy due to their invasive nature and potential for rapid progression. Accordingly, brain tumors classification is an essential step that allows doctors to perform appropriate treatment. Many studies have been done in the sector of medical image processing by employing computational methods to effectively segment and classify tumors. However, the larger amount of information collected by healthcare images prohibits the manual segmentation process in a reasonable time frame, reducing error measures in healthcare settings. Therefore, automated and efficient techniques for segmentation are crucial. In addition, various visual information, noisy images, occlusion, uneven image textures, confused objects, and other features may impact the process. Therefore, the implementation of deep learning provides remarkable results in medicinal image processing, particularly in the segmentation and classification process. However, conventional deep learning‐assisted methods struggle with complex structures and dimensional issues. Thus, this paper develops an effective technique for diagnosing brain tumors. The main aspect of the proposed system is to classify the brain tumor types by segmenting the affected regions of the raw images. This novel approach can be applied for various applications like diagnostic centers, decision‐making tools, clinical trials, medical research institutes, disease prognosis, and so on. Initially, the requisite images are collected from standard datasets and further, it is subjected to the segmentation period. In this stage, the Multi‐scale and Dilated TransUNet++ (MDTUNet++) model is employed to segment the abnormalities. Further, the segmented images are given into an Adaptive Dilated Dense Residual Attention Network (ADDRAN) to classify the brain tumor types. Here, to optimize the ADDRAN technique's parameters, an Improved Hermit Crab Optimizer (IHCO) is supported, which increases the accuracy rates of the overall network. Finally, the numerical examination is conducted to guarantee the robustness and usefulness of the designed model by contrasting it with other related techniques. For Dataset 1, the accuracy value attains 93.71 for the proposed work compared to 87.86 for CNN, 90.18 for DenseNet, and 89.56 and 90.96 for RAN and DRAN, respectively. Thus, supremacy has been achieved for the recommended system while detecting the brain tumor types.

A hybrid explainable model based on advanced machine learning and deep learning models for classifying brain tumors using MRI images

Brain tumors present a significant global health challenge, and their early detection and accurate classification are crucial for effective treatment strategies. This study presents a novel approach combining a lightweight parallel depthwise separable convolutional neural network (PDSCNN) and a hybrid ridge regression extreme learning machine (RRELM) for accurately classifying four types of brain tumors (glioma, meningioma, no tumor, and pituitary) based on MRI images. The proposed approach enhances the visibility and clarity of tumor features in MRI images by employing contrast-limited adaptive histogram equalization (CLAHE). A lightweight PDSCNN is then employed to extract relevant tumor-specific patterns while minimizing computational complexity. A hybrid RRELM model is proposed, enhancing the traditional ELM for improved classification performance. The proposed framework is compared with various state-of-the-art models in terms of classification accuracy, model parameters, and layer sizes. The proposed framework achieved remarkable average precision, recall, and accuracy values of 99.35%, 99.30%, and 99.22%, respectively, through five-fold cross-validation. The PDSCNN-RRELM outperformed the extreme learning machine model with pseudoinverse (PELM) and exhibited superior performance. The introduction of ridge regression in the ELM framework led to significant enhancements in classification performance model parameters and layer sizes compared to those of the state-of-the-art models. Additionally, the interpretability of the framework was demonstrated using Shapley Additive Explanations (SHAP), providing insights into the decision-making process and increasing confidence in real-world diagnosis.

A Novel Encoder Decoder Architecture with Vision Transformer for Medical Image Segmentation

Brain tumor image segmentation is one of the most critical tasks in medical imaging for diagnosis, treatment planning, and prognosis. Traditional methods for brain tumor image segmentation are mostly based on Convolution Neural Network (CNN), which have been proved very powerful but still have limitations to effectively capture long-range dependencies and complex spatial hierarchies in MRI images. Variability in the shape, size, and location of tumors may affect the performance and may get stuck into suboptimal outcomes. In these regards, new encoder-decoder architecture with the VisionTranscoder(ViT) is proposed, to enhance brain tumor detection and classification. The proposed VisionTranscoder exploits a transformer's ability in modeling global context through self-attention mechanisms, providing more inclusive interpretation of the intricate patterns in medical images and classification by capturing both local and global features. The proposed VisionTranscoder maintains the Vision Transformer in its encoder for processing images as sequences of patches to capture global dependencies often outside the view of traditional CNNs. Then the segmentation map is rebuilt at a high level of fidelity with the decoder through upsampling and skips connections to maintain detailed spatial information. The risk of overfitting is hugely reduced by design and advanced regularization techniques with extensive data augmentation. The dataset contains 7,023 human brain MRI images, all of which are in four different classes: glioma, meningioma, no tumor, and pituitary. Images from the 'no tumor' class, indicating an MRI scan without any detectable tumor, were taken from the Br35H dataset . The results show the efficiency of VisionTranscoder over a wide set of brain MRI scans, producing an accuracy of 98.5% with a loss of 0.05. This performance underlines the ability of it to accurately segment and classify a brain tumor without overfitting.

Multi-Class Brain Tumor Grades Classification Using a Deep Learning-Based Majority Voting Algorithm and Its Validation Using Explainable-AI.

Biopsy is considered the gold standard for diagnosing brain tumors, but its invasive nature can pose risks to patients. Additionally, tissue analysis can be cumbersome and inconsistent among observers. This research aims to develop a cost-effective, non-invasive, MRI-based computer-aided diagnosis tool that can reliably, accurately and swiftly identify brain tumor grades. Our system employs ensemble deep learning (EDL) within an MRI multiclass framework that includes five datasets: two-class (C2), three-class (C3), four-class (C4), five-class (C5) and six-class (C6). The EDL utilizes a majority voting algorithm to classify brain tumors by combining seven renowned deep learning (DL) models-EfficientNet, VGG16, ResNet18, GoogleNet, ResNet50, Inception-V3 and DarkNet-and seven machine learning (ML) models, including support vector machine, K-nearest neighbour, Naïve Bayes, decision tree, linear discriminant analysis, artificial neural network and random forest. Additionally, local interpretable model-agnostic explanations (LIME) are employed as an explainable AI algorithm, providing a visual representation of the CNN's internal workings to enhance the credibility of the results. Through extensive five-fold cross-validation experiments, the DL-based majority voting algorithm outperformed the ML-based majority voting algorithm, achieving the highest average accuracies of 100 ± 0.00%, 98.55 ± 0.35%, 98.47 ± 0.63%, 95.34 ± 1.17% and 96.61 ± 0.85% for the C2, C3, C4, C5 and C6 datasets, respectively. Majority voting algorithms typically yield consistent results across different folds of the brain tumor data and enhance performance compared to any individual deep learning and machine learning models.

Auto-Encoder Based Image Classification Technique for Classifying Brain Tumors

Auto-Encoder Based Image Classification Technique for Classifying Brain Tumors

Brain Glial Cell Tumor Classification through Ensemble Deep Learning with APCGAN Augmentation

Classification of brain tumor plays a vital role in medical imaging for accurate diagnosis, treatment, and monitoring. Deep learning approaches have gained significant traction in this industry because of their ability to extract relevant features from medical images. The research suggests employing an ensemble classifier with a weighted voting mechanism to categorize glial cell brain malignancies such as Astrocytoma, Glioblastoma multiforme, Oligodendroglioma, and Ependymoma. The proposed ensemble technique employs three main classifiers: Convolutional Neural Network (CNN), Deep Convolutional Long Short Term Memory (C-LSTM), and Deep Convolutional Neural Network + Conditional Random Fields (DCNN+CRF). Deep learning algorithms require a huge amount of input data to avoid overfitting. The Adaptive Progressive Convolutional Generative Adversarial Networks (APCGANs) are used to produce realistic artificial images to efficiently train the proposed methodology. Overall, the proposed ensemble method with weighted voting strategy consistently outperforms the other tested algorithms (CNN, C-LSTM, and DCNN+CRF). Ensemble method attained an accuracy of 99.4 %, recall - 99.1%, precision- 98.0%, and F1-score of 99.2%. Ensemble method consistently demonstrates superior performance in accurately classifying brain tumors, making it a promising algorithm for brain tumor analysis tasks.

Advanced Brain Tumor Classification in MR Images Using Transfer Learning and Pre-Trained Deep CNN Models.

Brain tumor classification is a crucial task in medical diagnostics, as early and accurate detection can significantly improve patient outcomes. This study investigates the effectiveness of pre-trained deep learning models in classifying brain MRI images into four categories: Glioma, Meningioma, Pituitary, and No Tumor, aiming to enhance the diagnostic process through automation. A publicly available Brain Tumor MRI dataset containing 7023 images was used in this research. The study employs state-of-the-art pre-trained models, including Xception, MobileNetV2, InceptionV3, ResNet50, VGG16, and DenseNet121, which are fine-tuned using transfer learning, in combination with advanced preprocessing and data augmentation techniques. Transfer learning was applied to fine-tune the models and optimize classification accuracy while minimizing computational requirements, ensuring efficiency in real-world applications. Among the tested models, Xception emerged as the top performer, achieving a weighted accuracy of 98.73% and a weighted F1 score of 95.29%, demonstrating exceptional generalization capabilities. These models proved particularly effective in addressing class imbalances and delivering consistent performance across various evaluation metrics, thus demonstrating their suitability for clinical adoption. However, challenges persist in improving recall for the Glioma and Meningioma categories, and the black-box nature of deep learning models requires further attention to enhance interpretability and trust in medical settings. The findings underscore the transformative potential of deep learning in medical imaging, offering a pathway toward more reliable, scalable, and efficient diagnostic tools. Future research will focus on expanding dataset diversity, improving model explainability, and validating model performance in real-world clinical settings to support the widespread adoption of AI-driven systems in healthcare and ensure their integration into clinical workflows.

Brain Tumor Detection: Integrating Machine Learning and Deep Learning for Robust Brain Tumor Classification

Accurate detection and classification of brain tumors are essential for timely diagnosis and effective treatment planning. This study presents an integrated framework leveraging both machine learning (ML) and deep learning (DL) models for brain tumor detection and classification using MRI images. Two publicly available datasets are utilized: one for binary classification (tumor vs. no tumor) and another for multiclass classification (glioma, meningioma, and pituitary tumors). Comprehensive preprocessing steps, including resizing, feature extraction using the Gray Level Co-occurrence Matrix (GLCM), and feature selection via Chi-square testing, were employed to optimize the dataset for modeling. Machine learning models such as Decision Trees, K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and AdaBoost were compared with deep learning architectures like Convolutional Neural Networks (CNNs) and the pre-trained VGG16 model. Hyperparameter optimization techniques, including grid search and the Adam optimizer, were used to enhance model performance. The models were evaluated using metrics such as accuracy, precision, recall, F1-score, Mean Squared Error (MSE), and Mean Absolute Error (MAE). Results indicate that the VGG16 model consistently outperformed other approaches, achieving high validation accuracy. This study highlights the potential of integrating ML and DL techniques for accurate and efficient brain tumor detection and classification, offering valuable tools for medical diagnostics.

Brain Tumour Classification in MRI: Self Improved Osprey Optimised U-Net Model for Segmentation and Fused DeepNet Model based Classification

Brain Tumour Classification in MRI: Self Improved Osprey Optimised U-Net Model for Segmentation and Fused DeepNet Model based Classification

Brain Tumor Classification: Leveraging Transfer Learning via EfficientNet-B0 Pretrained Model

Brain Tumor Classification: Leveraging Transfer Learning via EfficientNet-B0 Pretrained Model

Federated learning for brain tumor segmentation and classification : A statistical approach

Enhancing the chances of survival for cancer patients requires early brain tumour identification. This study suggests a federated learning-based method to deal with classification problems caused by overfitting. With the use of the BRATS 2019 and 2020 datasets, the method successfully divides and categorises brain tumours. Data preprocessing involves min-max normalization, followed by CNN segmentation from MRI images. Extracted features undergo shape, texture, and Resnet-50-based feature extraction. These features are then inputted into an LSTM classifier, utilizing Federated Learning for improved classification. Findings indicate that the suggested FL utilizing the CNN-LSTM model outperforms other techniques like U-net and ensemble models, achieving high accuracy: 99.59% on BRATS 2019 and 99.61% on BRATS 2020 datasets.

SAlexNet: Superimposed AlexNet using Residual Attention Mechanism for Accurate and Efficient Automatic Primary Brain Tumor Detection and Classification

SAlexNet: Superimposed AlexNet using Residual Attention Mechanism for Accurate and Efficient Automatic Primary Brain Tumor Detection and Classification

SwinBTC: Transfer Learning to Brain Tumor Classification for Healthcare Electronics Using Augmented MR Images

SwinBTC: Transfer Learning to Brain Tumor Classification for Healthcare Electronics Using Augmented MR Images

AI-Powered brain tumor classification and predictive system using convolutional neural networks model

One of the most dangerous forms of cancer, brain tumors are brought on by abnormal cell partitioning that is not only uncontrolled but also abnormal. Recent developments in deep learning have been of great use to the healthcare industry, notably diagnostic imaging technology, which is used to diagnose a wide variety of illnesses. There is a good chance that task CNN is the deep learning model that is applied the most frequently and extensively for image recognition. In a similar manner, images obtained from brain MRI scanning are classified by our research team through the utilization of CNN, data augmentation, and image processing approaches. Through the use of CNN, we tested the performance of the scratch CNN model. Despite the fact that the analysis was conducted on a relatively small dataset, the findings reveal that our model's accuracy is quite successful and has incredibly low complexity rates. It achieved an accuracy of 99.5%, which is significantly higher than the other machine learning

Context aware machine learning techniques for brain tumor classification and detection – A Review

Context aware machine learning techniques for brain tumor classification and detection – A Review

DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

DieT Transformer model with PCA-ADE integration for advanced multi-class brain tumor classification

Explainable AI and deep learning for brain tumour classification using grad-CAM visualization

Explainable AI and deep learning for brain tumour classification using grad-CAM visualization

Artificial Intelligence (AI): Brain Tumor Detection

The detection and diagnosis of brain tumors, a critical medical challenge, have greatly benefited from the application of Artificial Intelligence (AI). This review paper explores the advancements, methods, and technologies of AI in the detection and classification of brain tumors from medical imaging modalities. It also highlights the importance of machine learning (ML) and deep learning (DL) algorithms, particularly Convolutional Neural Networks (CNNs), in improving diagnostic accuracy, early detection, and prognosis prediction. Moreover, the paper addresses challenges and future directions in integrating AI with clinical practices for brain tumor management.

Integrating Swin Transformer with Fuzzy Gray Wolve Optimization for MRI Brain Tumor Classification

Integrating Swin Transformer with Fuzzy Gray Wolve Optimization for MRI Brain Tumor Classification

Revolutionizing MRI Brain Tumor Detection Through GAN Augmentation and Smart Algorithms.

The accurate detection and classification of brain tumors in Magnetic Resonance Technology is critical to accurate diagnosis and therapy planning. However, the absence of annotated MR imaging datasets presents a significant challenge for advanced machine learning models, which require a range of data to achieve high accuracy and generalizability. This study provides a novel data augmentation method to enhance MR images for improved tumor identification by combining noise-to-image and image-to-image Generative Adversarial Networks (GANs). Noise-to-image GANs generate synthetic MR images from random noise, expanding the dataset with a range of anatomical variations, whereas image-to-image GANs refine and enhance existing MR images, highlighting important features relevant to tumor detection. The enlarged set of input data was taken to teach CNN, which was then correlated to a baseline model built solely on the original dataset. The GAN-augmented model beat the baseline cancer sorting and division tasks, as measured by parameters such as accuracy, recall, F1-score, and Dice coefficient. An ablation study verified the independent contributions of noise-to-image and image-to-image GANs, demonstrating that both types of augmentation operate together to improve model performance. This work illustrates the promise of GAN-based augmentation in medical imaging by giving a realistic solution to data restrictions and improving the robustness of deep learning models in brain tumor detection. This technology provides the way for more accurate and reliable based on artificial intelligence tools for diagnosis that can help medical practitioners make clinical decisions by integrating alternative GAN techniques. Key Words: GAN-based augmentation, Deep learning, Image augmentation techniques, Generative adversarial networks (GANs)