12/07/2026
MRI Brain Tumor Detection using Machine Learning.
This project uses **Python**, **OpenCV**, **HOG (Histogram of Oriented Gradients)**, and a **Decision Tree Classifier** to analyze MRI brain images and classify them into their respective categories through an end-to-end machine learning pipeline.
✨ **Project Features**
🔹 MRI image preprocessing using OpenCV
🔹 Grayscale image conversion and resizing (128×128)
🔹 HOG feature extraction for image representation
🔹 Label encoding and feature scaling
🔹 Decision Tree model training and testing
🔹 Performance evaluation using Accuracy, Precision, Recall, and F1-Score
🔹 Model saving with Joblib for future predictions
💻 **Tech Stack**
🐍 Python | OpenCV | NumPy | Scikit-learn | Scikit-image | Joblib
📚 **What I Learned**
✔️ Image preprocessing techniques
✔️ Feature extraction using HOG
✔️ Building an image classification model
✔️ Training and evaluating Machine Learning models
✔️ Saving and reusing trained models for predictions
This project helped me strengthen my knowledge of **Machine Learning**, **Computer Vision**, and **Medical Image Analysis** while building a practical AI solution.
⚠️ **Disclaimer:** This project is developed for educational and research purposes only. It is not intended for medical diagnosis or clinical decision-making.
📂 GitHub Repository: **[Add Your GitHub Repository Link Here]**
I would love to hear your feedback! If you like this project, please like, comment, and share. Your support motivates me to keep learning and building more AI and Machine Learning projects.
