# Brain Tumor Detection and Classification

**Type:** Topics  
**Canonical URL:** https://scholariq.org/topics/brain-tumor-detection-and-classification/

## Facts

| Field | Value |
| --- | --- |
| Description | This cluster of papers focuses on the classification of brain tumor type and grade using various techniques such as MRI, deep learning, convolutional neural networks, feature extraction, and machine learning. The research aims to improve the accuracy and efficiency of brain tumor classification for better diagnosis and treatment. |
| Domain | Life Sciences |
| Field | Neuroscience |
| OpenAlex ID | t12702 |
| Works | 259 |

## Topic papers all

Showing 15 of 259.

- [The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)](https://scholariq.org/papers/the-multimodal-brain-tumor-image-segmentation-benchmark-brats/)
- [CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope](https://scholariq.org/papers/cnn-variants-for-computer-vision-history-architecture-application-challenges-and/)
- [Convolutional Neural Network (CNN) for Image Detection and Recognition](https://scholariq.org/papers/convolutional-neural-network-cnn-for-image-detection-and-recognition/)
- [Towards a guideline for evaluation metrics in medical image segmentation](https://scholariq.org/papers/towards-a-guideline-for-evaluation-metrics-in-medical-image-segmentation/)
- [Blood vessel segmentation algorithms — Review of methods, datasets and evaluation metrics](https://scholariq.org/papers/blood-vessel-segmentation-algorithms-review-of-methods-datasets-and-evaluation/)
- [Optimal deep learning model for classification of lung cancer on CT images](https://scholariq.org/papers/optimal-deep-learning-model-for-classification-of-lung-cancer-on-ct-images/)
- [ISLES 2015 - A public evaluation benchmark for ischemic stroke lesion segmentation from multispectral MRI](https://scholariq.org/papers/isles-2015-a-public-evaluation-benchmark-for-ischemic-stroke-lesion-segmentation/)
- [A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification](https://scholariq.org/papers/a-hybrid-feature-extraction-method-with-regularized-extreme-learning-machine-for/)
- [A Review on a Deep Learning Perspective in Brain Cancer Classification](https://scholariq.org/papers/a-review-on-a-deep-learning-perspective-in-brain-cancer-classification/)
- [Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists](https://scholariq.org/papers/multimodal-brain-tumor-classification-using-deep-learning-and-robust-feature/)
- [Convolutional neural networks for medical image analysis: State-of-the-art, comparisons, improvement and perspectives](https://scholariq.org/papers/convolutional-neural-networks-for-medical-image-analysis-state-of-the-art/)
- [Dynamic Adaptive DNN Surgery for Inference Acceleration on the Edge](https://scholariq.org/papers/dynamic-adaptive-dnn-surgery-for-inference-acceleration-on-the-edge/)
- [Iterative enhancement fusion-based cascaded model for detection and localization of multiple disease from CXR-Images](https://scholariq.org/papers/iterative-enhancement-fusion-based-cascaded-model-for-detection-and-localization/)
- [Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images](https://scholariq.org/papers/multimodal-and-multiscale-deep-neural-networks-for-the-early-diagnosis-of/)
- [A review on brain tumor segmentation of MRI images](https://scholariq.org/papers/a-review-on-brain-tumor-segmentation-of-mri-images/)

## Topic primary papers

Showing 15 of 120.

- [Convolutional Neural Network (CNN) for Image Detection and Recognition](https://scholariq.org/papers/convolutional-neural-network-cnn-for-image-detection-and-recognition/)
- [Optimal deep learning model for classification of lung cancer on CT images](https://scholariq.org/papers/optimal-deep-learning-model-for-classification-of-lung-cancer-on-ct-images/)
- [A Review on a Deep Learning Perspective in Brain Cancer Classification](https://scholariq.org/papers/a-review-on-a-deep-learning-perspective-in-brain-cancer-classification/)
- [Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists](https://scholariq.org/papers/multimodal-brain-tumor-classification-using-deep-learning-and-robust-feature/)
- [A review on brain tumor segmentation of MRI images](https://scholariq.org/papers/a-review-on-brain-tumor-segmentation-of-mri-images/)
- [A Hybrid Deep Learning-Based Approach for Brain Tumor Classification](https://scholariq.org/papers/a-hybrid-deep-learning-based-approach-for-brain-tumor-classification/)
- [Deep Transfer Learning Approaches in Performance Analysis of Brain Tumor Classification Using MRI Images](https://scholariq.org/papers/deep-transfer-learning-approaches-in-performance-analysis-of-brain-tumor/)
- [Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images](https://scholariq.org/papers/active-deep-neural-network-features-selection-for-segmentation-and-recognition/)
- [Microscopic brain tumor detection and classification using <scp>3D CNN</scp> and feature selection architecture](https://scholariq.org/papers/microscopic-brain-tumor-detection-and-classification-using-scp-3d-cnn-scp-and/)
- [A decision support system for multimodal brain tumor classification using deep learning](https://scholariq.org/papers/a-decision-support-system-for-multimodal-brain-tumor-classification-using-deep/)
- [Deep Learning Framework for Alzheimer’s Disease Diagnosis via 3D-CNN and FSBi-LSTM](https://scholariq.org/papers/deep-learning-framework-for-alzheimer-s-disease-diagnosis-via-3d-cnn-and-fsbi/)
- [Brain tumor detection and multi‐classification using advanced deep learning techniques](https://scholariq.org/papers/brain-tumor-detection-and-multi-classification-using-advanced-deep-learning/)
- [Classification of Brain Tumor from Magnetic Resonance Imaging Using Vision Transformers Ensembling](https://scholariq.org/papers/classification-of-brain-tumor-from-magnetic-resonance-imaging-using-vision/)
- [Brain Tumor Classification via Statistical Features and Back-Propagation Neural Network](https://scholariq.org/papers/brain-tumor-classification-via-statistical-features-and-back-propagation-neural/)
- [Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art](https://scholariq.org/papers/deep-learning-for-brain-tumor-segmentation-a-survey-of-state-of-the-art/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.