JeeKay/Malaria-classification

1---2title: Malaria Classification3emoji: 🧬4colorFrom: green5colorTo: red6sdk: streamlit7sdk_version: "1.45.1"8app_file: app/app.py9pinned: false10---11 12# 🧬 Malaria Cell Classifier with Grad-CAM & Streamlit UI13 14A deep learning-based malaria detection system using ResNet50 and Grad-CAM explainability.15 16## 🚀 Features17 18- ✅ Binary classification of blood smear images (`Infected` / `Uninfected`)19- 🔍 Grad-CAM visualizations to highlight infected regions20- 🌐 Interactive Streamlit web interface21- 📦 Easy-to-deploy structure22 23## 🛠️ Built With24 25- [PyTorch](https://pytorch.org/)26- [Streamlit](https://streamlit.io/)27- [Grad-CAM](https://arxiv.org/abs/1610.02391)28- [ResNet50](https://pytorch.org/vision/stable/models.html)29 30## 📦 Dataset31 32Uses the [Malaria Cell Images Dataset](https://www.kaggle.com/iarunava/cell-images-for-detecting-malaria)33 34## 📁 Folder Structure35 36Place raw images in:37data/cell_images/38    ├── Parasitized/39    └── Uninfected/40 41## Here's a quick preview of the app in action:42 43![Malaria Classifier Demo](demo.gif)44 45## 🧪 Usage46 47## 🛠️ Requirements48 49Install dependencies:50 51```bash52pip install torch torchvision streamlit opencv-python matplotlib scikit-learn53```54