Brain Tumor Classification Using CNNs

Overview

Developed a comprehensive brain tumor classification system using deep learning techniques for medical image analysis. This project demonstrates the application of transfer learning and advanced data augmentation techniques to achieve high accuracy in medical diagnosis.

Key Features

• Transfer Learning: Utilized pretrained VGG16 and ResNet architectures
• Advanced Data Augmentation: Implemented sophisticated augmentation techniques to improve model generalization
• Normalization Techniques: Applied advanced normalization methods for medical imaging
• High Accuracy: Achieved 92% validation accuracy on brain tumor classification

Technical Implementation

Architecture

• Base Models: VGG16 and ResNet pretrained on ImageNet
• Fine-tuning: Custom classification layers for brain tumor detection
• Data Pipeline: Robust preprocessing and augmentation pipeline

Technologies Used

• Framework: PyTorch
• Models: VGG16, ResNet
• Domain: Medical Imaging
• Techniques: Transfer Learning, Data Augmentation

Results

• Validation Accuracy: 92%
• Model Performance: Robust classification across different tumor types
• Clinical Relevance: Potential application in medical diagnosis support systems

Impact

This project demonstrates the potential of deep learning in medical imaging, providing a foundation for computer-aided diagnosis systems that could assist healthcare professionals in brain tumor detection and classification.