Mazen Asag

Project Details

🐔 Chicken Disease Classification



📌 Problem Statement
This project focuses on classifying chicken health conditions into Healthy and Coccidiosis using deep learning. The objective is to fine-tune a VGG16 model to achieve high classification accuracy, assisting poultry farmers in early disease detection.

📊 Data Collection
Dataset Source: Kaggle - Chicken Disease
https://www.kaggle.com/datasets/allandclive/chicken-disease-1

Features Description:

• Image: Visual input of chicken (Healthy or Coccidiosis infected)
• Label: Binary class - Healthy or Coccidiosis

Dataset Summary:
- Binary classification task
- Images resized and normalized
- Data augmentation applied for better generalization

📈 Model Performance:

• Accuracy: 93.8%
• Precision: 92.5%
• Recall: 94.2%
• F1 Score: 93.3%

🔍 Key Insights:

• Fine-tuned VGG16 performed best on limited data
• Data augmentation increased robustness
• Transfer learning enabled fast convergence and high accuracy

📊 EDA & Model Training:
- EDA Notebook
- Model Training Notebook
- Grad-CAM Visualizations

🛠️ How to Run the Project:
1. Clone the repository:
git clone https://github.com/Mazenasag/Chicken_Disease_Classification.git
2. Navigate to the folder:
cd Chicken_Disease_Classification

👤 Contributor: Mazen Asag
📜 License: MIT License

You're welcome to explore, use, and improve the project! 🧠💡