{"id":25486616,"url":"https://github.com/nishithat-28/glaucoma-detection-using-deep-learning","last_synced_at":"2026-05-04T11:32:00.596Z","repository":{"id":277732566,"uuid":"932840891","full_name":"nishithat-28/Glaucoma-Detection-using-Deep-Learning","owner":"nishithat-28","description":"Glaucoma Detection using Deep Learning |  A ResNet50-based deep learning model for automated glaucoma diagnosis from fundus images. 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Our project uses a **Convolutional Neural Network (CNN)** based on **ResNet50** to classify images into:\n- ✅ **Normal**  \n- ⚠️ **Glaucoma-Affected**\n\n## **🛠️ Tech Stack**\n- **Programming Language:** Python  \n- **Deep Learning Framework:** TensorFlow \u0026 Keras   \n- **Model Architecture:** ResNet50   \n- **Preprocessing:** OpenCV (CLAHE, Image Augmentation)   \n- **Deployment:** Streamlit   \n\n## **📂 Dataset**\n\n### 📌 **Source**\nThis dataset contains **fundus images** from **3 combined sources** (ACRIMA, DRISHTI-GS, ORIGA).\n\n- Kaggle: [Glaucoma Classification Datasets](https://www.kaggle.com/datasets/ayush02102001/glaucoma-classification-datasets)\n\nIt includes:\n- **\"Normal\"** 🟢 (Healthy eyes)\n- **\"Glaucoma\"** 🔴 (Affected eyes)\n\n🔄 **Preprocessing Techniques:**  \n- **CLAHE** - Enhances contrast  \n- **Image Augmentation** -  Random rotations, flips, and zooms to improve generalization\n- **Resizing** - Standardizing images to **256x256**  \n\n## **🔢 Training Results**\n- **Final Training Accuracy:** **98.86%**\n- **Final Validation Accuracy:** **88.89%**\n- **Test Accuracy:** **77.64%**\n\n## **📌 How to Run the Project**\n### **🔧 Install Dependencies**\n```sh\npip install -r requirements.txt\n```\n\n### **🏃 Run the Streamlit App**\n```sh\npython -m streamlit run app.py\n```\n\n## **🎛️ Streamlit UI**\nWe developed an **interactive UI** using **Streamlit**, allowing users to:\n- 🔍 **Upload a fundus image**  \n- 🖼️ **Preview the image**  \n- 🧠 **Get instant glaucoma detection results with confidence score**  \n\n## **🖥️ UI Preview**\n \nThe landing screen of the Glaucoma Detection web app. It provides a brief introduction and guides the user to upload a retinal image.\n\n![Home Page](output_images/public_home.png)\n\n---\n\n## 🧾 License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnishithat-28%2Fglaucoma-detection-using-deep-learning","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnishithat-28%2Fglaucoma-detection-using-deep-learning","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnishithat-28%2Fglaucoma-detection-using-deep-learning/lists"}