The document discusses brain tumor detection using deep learning, highlighting its significance for early diagnosis, challenges in conventional methods, and the role of convolutional neural networks (CNNs) and advanced techniques like transformer architectures. It covers dataset preparation, training processes, evaluation metrics, and presents case studies demonstrating the effectiveness of models like VGG and Swin-ViT. The conclusion emphasizes the future potential of AI in real-time diagnosis and personalized treatment predictions.

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 through
conventional methods

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

4. Role of Deep Learning in Medical
Imaging
• • Overview of deep learning’s role in analyzing
medical images
• • Advantages: speed, accuracy, reproducibility
• • Successful applications in other medical
fields

5. Dataset for Brain Tumor Detection
• • Description of MRI image dataset
• • Preprocessing steps: resizing, normalization,
augmentation
• • Dataset collection and labeling challenges

6. Convolutional Neural Networks
(CNNs) Overview
• • Basic architecture of CNNs
• • Why CNNs are effective for image
recognition
• • How CNNs process medical images

7. Popular CNN Architectures for
Brain Tumor Detection
• • Overview of VGG, ResNet, and other
architectures
• • Performance on medical image datasets
• • Selection criteria for architectures

8. Advanced Techniques for Enhanced
Detection
• • Swin Transformer and Vision Transformers
(ViT)
• • 3D Convolutions for volumetric MRI data
• • Deformable convolutions for flexible feature
extraction

9. Training and Evaluation Metrics
• • Data split: 70% training, 30% testing
• • Evaluation metrics: accuracy, sensitivity,
specificity, F1-score
• • Importance of balanced datasets

10. Example Deep Learning Model
Workflow
• • Data preprocessing (label encoding,
normalization)
• • Model architecture and training steps
• • Hyperparameter tuning (learning rate, batch
size, epochs)

11. Brain Tumor Detection Using VGG
• • Specifics of using VGG architecture
• • Example results: accuracy, loss
• • Model adjustments for MRI image
processing

12. Case Study: SWIN-ViT for Brain
Tumor Detection
• • Explain Swin Transformer’s approach to
patch processing
• • Benefits for medical images
• • Results in brain tumor datasets

13. Results and Findings
• • Comparative results: CNNs vs. Transformers
• • Graphs for accuracy, loss, and metrics
• • Training/testing challenges encountered

14. Future Directions in Brain Tumor
Detection Using AI
• • Potential for real-time diagnosis
• • Integration with radiology workflows
• • Personalized treatment predictions

15. Conclusion
• • Recap of deep learning's impact on brain
tumor detection
• • Summary of key findings and limitations
• • Future prospects and closing remarks