Chest X-Ray Classification using YOLOv11 | Pneumonia vs Normal Detection | Full Tutorial

🚀 Chest X-Ray Classification using YOLOv11 | Pneumonia vs Normal | Complete Training + Testing Tutorial

In this video, we build a YOLOv11 Classification model to detect Pneumonia from Chest X-ray images using a custom dataset.

You will learn how to:
✔ Download dataset automatically using gdown
✔ Organize dataset into train/test folders
✔ Train YOLOv11 classification model
✔ Perform augmentation (rotation, flip, shear, translate, scale)
✔ Test the model on random images
✔ Visualize predictions in a 4×4 grid

This is a perfect end-to-end guide for beginners and ML practitioners working on medical imaging & deep learning.

🧠 Technologies Used
Python
YOLOv11 Classification
Ultralytics
OpenCV
Matplotlib
YAML
Google Colab

🔍 What You Will Learn
How to prepare medical X-ray datasets
How to perform data augmentation for classification
How to train YOLO11-cls models
How to evaluate classification accuracy
How to run inference on test images
How to build custom Pneumonia detection model

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📌 GitHub Repository : https://github.com/NitinCVOrbit/Chest-X-Ray-Classification-using-YOLOv11-Pneumonia-vs-Normal

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