The document provides an overview of brain tumor detection using deep learning, highlighting the challenges and importance of early diagnosis. It discusses the role of convolutional neural networks (CNNs) and advanced techniques like vision transformers for improving detection accuracy. Additionally, it outlines a case study on VGG architecture and future directions, emphasizing the potential for real-time diagnostics and personalized treatment in medical AI.

1. Brain Tumor Detection Using
Deep Learning
Presented by Bhavesh Agrawal
Date: November 11, 2024

2. Introduction to Brain Tumors
• • Overview of brain tumors
• • Importance of early detection
• • Challenges in diagnosis

3. Challenges in Brain Tumor
Detection
• • Variability in tumor shapes and sizes
• • Differences in tissue types (benign vs.
malignant)
• • Limitations in human-based image analysis

4. Role of Deep Learning in Medical
Imaging
• • Deep learning enhances accuracy and
efficiency
• • Reliable analysis and reproducibility
• • Successful applications in medical diagnosis

5. Dataset for Brain Tumor Detection
• • MRI image datasets, including preprocessing
• • Steps: resizing, normalization, augmentation
• • Collection and labeling challenges

6. Convolutional Neural Networks
(CNNs) Overview
• • CNNs are effective for image recognition
• • Layers: convolutions, pooling, fully
connected
• • Applications in medical images

7. Popular CNN Architectures for
Brain Tumor Detection
• • VGG, ResNet, EfficientNet and more
• • Optimized for medical images
• • Performance on MRI datasets

8. Advanced Techniques for Enhanced
Detection
• • Vision Transformers (ViT), 3D Convolutions
• • Swin Transformers and deformable
convolutions
• • Flexible for diverse data types

9. Training and Evaluation Metrics
• • Training split: 70% train, 30% test
• • Evaluation: accuracy, specificity, sensitivity
• • Importance of balanced datasets

10. Example Deep Learning Model
Workflow
• • Preprocessing, architecture selection,
training
• • Hyperparameter tuning
• • Validation and performance optimization

11. Brain Tumor Detection Using VGG
• • VGG-specific architecture adjustments
• • Results: high accuracy, low loss
• • Tailoring CNNs to MRI images

12. Case Study: SWIN-ViT for Brain
Tumor Detection
• • Patch-based processing approach
• • Effective for detailed brain tumor images
• • Improved accuracy and efficiency

13. Results and Findings
• • CNNs vs. Transformers results
• • High accuracy rates in detection
• • Visualization of performance metrics

14. Future Directions in Brain Tumor
Detection
• • Real-time diagnostics in hospitals
• • Integrating with treatment predictions
• • Prospects for personalized treatment

15. Conclusion
• • Summary of deep learning impact in
detection
• • Key findings and challenges
• • Future implications in medical AI