Build a CNN from Scratch to Detect and Localize Brain Tumors Using MRI Scans | Python, AI, ML
In this video, we will build a Convolutional Neural Network (CNN) from scratch to analyze and detect brain tumors from MRI scans. This project aims to deepen your understanding of how image segmentation operates from the ground up for pixel-level classification, transfer learning, and other AI concepts.
Throughout this hands-on experience, we'll learn powerful machine learning concepts, including RestNet50, CNN, ResNet, ResUnet, Transfer learning and much more. Additionally, we'll explore the art of data visualization using tools like Pyplot and Seaborn. So sit back, relax, and enhance your Machine Learning skills with us!
Materials / References 📚:
Github Codes and Datasets (give it a star ⭐):
MRI - https://github.com/mendsalbert/MRI-sc...
MRI-Dataset - https://drive.google.com/drive/folder...
Visual Studio website - https://code.visualstudio.com/
Python official website - https://www.python.org/
In this single video, you'll ✅:
-Build a Convolutional Neural Network (CNN) from scratch and use it to detect brain tumors from MRI scans.
-Gain an understanding of how transfer learning functions by utilizing the ResNet-50 architecture along with ImageNet weights and features.
-Delve into the inner workings of image segmentation.
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Timestamps
00:00:00 - Intro
00:01:35 - Project
00:23:09 - CNN Explained
00:26:58 - ResNet Explained
00:30:10 - Transfer Learning Explained
00:32:39 - ResUnet Explained
00:35:05 - Project