Brain tumor segmentation and classification

Brain Tumor Detection and Classification Using MRI

This project focuses on the automated detection and classification of brain tumors using advanced image processing and machine learning techniques. Built in MATLAB, it combines classical segmentation methods (Otsu, K-means, Fuzzy C-means) with state-of-the-art deep learning models (ResNet-50, Inception v3) for precise tumor identification and classification. The pipeline includes preprocessing, feature extraction using GLCM and DWT, and evaluation using metrics such as accuracy and F1 score.

This project is designed to aid medical professionals with an efficient, non-invasive diagnostic tool for early detection and improved patient outcomes. It aligns with SDG Goal 3: Good Health and Well-being.

Badges

Features

• Comprehensive Segmentation: Implements multiple methods like K-means clustering, Fuzzy C-means, and Watershed for precise tumor localization in MRI scans.
• Feature Extraction: Utilizes Gray Level Co-occurrence Matrix (GLCM) and Discrete Wavelet Transform (DWT) for enhanced image feature analysis.
• Advanced Classification: Leverages pre-trained models (ResNet-50, Inception V3) for multi-class tumor classification (glioma, meningioma, pituitary, and no tumor).
• Real-Time Visualization: Includes visual outputs for segmentation and classification results, aiding interpretability.
• Performance Metrics: Evaluates models using metrics like accuracy, precision, recall, F1-score, and confusion matrices for robust validation.

Project Workflow

1. Data Preprocessing: MRI images are normalized and augmented to improve model generalization.
2. Segmentation: Various algorithms isolate tumor regions effectively for subsequent analysis.
3. Feature Extraction: Captures texture and structural features essential for tumor differentiation.
4. Classification: Deep learning models classify the extracted regions into predefined tumor categories.
5. Evaluation: Performance is assessed through metrics and visualization for quality assurance.

References

1. Brain Tumor Detection Using Machine Learning and CNN – Combines ML algorithms and CNN for tumor detection with detailed documentation.
2. Deep Learning for Brain Tumor Classification – Explores CNN architectures like ResNet-50 and DenseNet-121 for brain tumor analysis.
3. Brain Tumor Classification with CNN – Focuses on classification using CNNs and provides scripts for model deployment.
4. Brain Tumor Image Segmentation and Classification – Includes segmentation (UNet-VGG16) and classification models for enhanced medical imaging.

These repositories provide additional insights and complementary approaches to enhance your project documentation and implementation.