Machine Learning Approach to Detect Brain Tumors: Techniques and Applications
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Presentation Link: https://youtu.be/wsCRomjhorM Credit: Amit Golder, Tanvir Niaz Khan, Marcel David Baori This presentation explores how Machine Learning (ML) is revolutionizing brain tumor detection. It covers key ML techniques such as deep learning, image processing, and classification algorithms used in medical imaging. Learn about advancements in AI-driven diagnostics, accuracy improvements, and real-world applications in healthcare.
Machine Learning Approach to Detect Brain Tumors: Techniques and Applications
- 2. Amit Golder 201-15-3058 Marcel David Baroi 201-15-3421 Tanvir Niaz Khan 201-15-3593 Team Dropout
- 3. A Machine Learning Approach to Detect Brain Tumor
- 5. A brain tumor is a mass or growth of abnormal cells in your brain Introductio n
- 7. Static in Primary Location Do not Spread Benig n
- 8. Not Static in Primary Location Spread Maligna nt
- 10. 1.8 Million affected with Primary Brain Tumor USA
- 12. 18,20 0 will Die
- 13. Detect Brain Tumors at primary stage Objective s
- 25. Start End Data Preprocessing Data Split Validation Basic Algorithm ResNet-50 MobileNet Inception V3 Performance Evolution Performance Compare Detection Train Test Input Proposed Model
- 27. Inception v3:
- 28. MobileN et
- 29. ResNet5 0
- 30. Result and Accuracy Inception V3 98% MobileNet 97.67% ResNet 50 75.17%