---
title: Fish Disease Detection AI
emoji: 🐟
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 5.7.1
app_file: app.py
pinned: false
license: mit
tags:
  - computer-vision
  - deep-learning
  - vgg16
  - fish-disease
  - grad-cam
  - explainable-ai
  - medical-imaging
---

# 🐟 Fish Disease Detection AI

[![Python](https://img.shields.io/badge/Python-3.8+-blue.svg)](https://www.python.org/downloads/)
[![PyTorch](https://img.shields.io/badge/PyTorch-2.5.1-red.svg)](https://pytorch.org/)
[![Gradio](https://img.shields.io/badge/Gradio-5.7.1-orange.svg)](https://gradio.app/)
[![License](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)

**AI-powered fish disease detection system combining VGG16 CNN, Grad-CAM explainability, and Gemini AI for comprehensive diagnosis and treatment recommendations.**

---

## 🎯 Key Features

### 🎓 **High Accuracy**
- **98.65% test accuracy** on 8 fish disease classes
- Trained on **5,000+ annotated images**
- Robust to various image conditions

### 🔬 **Explainable AI**
- **Grad-CAM heatmap visualization** shows exactly where the model is looking
- Highlights disease-relevant areas (lesions, discoloration, abnormalities)
- Builds trust through transparency

### 🤖 **AI-Powered Treatment**
- **Google Gemini 2.0** generates disease-specific treatment protocols
- Immediate actions, medication recommendations, and preventive measures
- Expected recovery rates and timelines

### ⚡ **Real-Time Performance**
- **~2-3 second inference** on GPU
- Supports batch processing
- Web-based interface accessible anywhere

---

## 🦠 Detected Diseases

| Disease | Description | Severity |
|---------|-------------|----------|
| **Aeromoniasis** | Bacterial infection causing hemorrhaging | High |
| **Bacterial Gill Disease** | Respiratory issues, gill damage | High |
| **Bacterial Red Disease** | External lesions and ulcers | Medium |
| **EUS** | Epizootic Ulcerative Syndrome | Critical |
| **Healthy Fish** | No disease detected | None |
| **Parasitic Diseases** | External/internal parasites | Medium |
| **Saprolegniasis Fungal** | Fungal infection (cotton-like growth) | Medium |
| **Viral White Tail** | Viral infection affecting tail | High |

---

## 🚀 How to Use

### 1️⃣ **Upload Image**
Upload a clear, well-lit photo of your fish (JPG/PNG, max 10MB)

### 2️⃣ **Analyze**
Click "Analyze Fish" button for instant diagnosis

### 3️⃣ **Review Results**
- **Disease prediction** with confidence score
- **Probability breakdown** for all 8 diseases
- **Grad-CAM heatmap** showing model focus areas
- **AI treatment recommendations** with detailed protocols

---

## 📊 Model Architecture

Input Image (224×224 RGB)
↓
VGG16 Backbone (Pretrained on ImageNet)
↓
Feature Extraction (4096-dim)
↓
Custom Classification Head
↓
8-Class Softmax Output
↓
Grad-CAM Activation Mapping


**Technical Specifications:**
- **Base Model:** VGG16 (transfer learning)
- **Input Size:** 224×224 pixels
- **Normalization:** ImageNet mean/std
- **Framework:** PyTorch 2.5.1
- **Device:** CUDA/CPU compatible

---

## 📈 Performance Metrics

| Metric | Score |
|--------|-------|
| **Test Accuracy** | 98.65% |
| **Precision (avg)** | 98.2% |
| **Recall (avg)** | 98.1% |
| **F1-Score (avg)** | 98.15% |
| **Training Samples** | 5,000+ |
| **Validation Samples** | 1,000+ |
| **Test Samples** | 500+ |

---

## 🎯 Confidence Thresholds

The system uses a **70% confidence threshold** for reliable diagnoses:

- **≥ 80%** - 🟢 High confidence (Very reliable)
- **70-79%** - 🟡 Good confidence (Reliable)  
- **< 70%** - 🔴 Low confidence (Requires verification)

When confidence is below 70%, the system:
- Shows top 3 disease candidates
- Provides general guidelines
- Recommends professional consultation

---

## 🔬 Grad-CAM Visualization

**Understanding the Heatmap:**

- 🔴 **Red areas** - High importance (disease symptoms, lesions)
- 🟡 **Yellow areas** - Moderate importance
- 🟢 **Green/Blue areas** - Low importance

The heatmap proves the model focuses on actual pathological features, not spurious correlations.

---

## 🛠️ Technical Details

### Dependencies

torch==2.5.1
torchvision==0.20.1
gradio==5.7.1
google-generativeai==0.8.3
pillow==11.0.0
opencv-python-headless==4.10.0.84
numpy==1.26.4
python-dotenv==1.0.0


### Environment Setup
This application requires a **Gemini API key** for treatment recommendations. Set it as an environment variable:

GEMINI_API_KEY=your_api_key_here

---

## ⚠️ Medical Disclaimer

**This is an AI diagnostic tool for preliminary screening only.**

### ✅ Use For:
- Initial disease screening
- Educational purposes
- Research and development
- Aquaculture monitoring

### ❌ Do NOT Use For:
- Definitive medical diagnosis
- Treatment without professional consultation
- Emergency veterinary decisions

**Always consult a qualified aquaculture veterinarian for:**
- Professional diagnosis confirmation
- Treatment plan approval  
- Medication dosage recommendations
- Emergency health situations

---

## 🎓 Research & Citation

This project is part of research on AI-assisted aquaculture diagnostics and explainable deep learning.

**BibTeX Citation:**

@software{fish_disease_detection_2025,
  author = {Justin Mathais},
  title = {Fish Disease Detection AI: VGG16 with Grad-CAM Explainability},
  year = {2025},
  url = {https://github.com/YOUR_USERNAME/fish-disease-detection}
}

---

## 📧 Contact & Support

- **Author:** Your Name
- **Email:** your.email@example.com
- **GitHub:** [github.com/mathaisjustin](https://github.com/mathaisjustin)
- **Issues:** [Report bugs or suggest features](https://github.com/mathaisjustin/fish-disease-detection/issues)

---

## 📜 License

This project is licensed under the **MIT License** - see [LICENSE](LICENSE) file for details.

---

## 🙏 Acknowledgments

- **VGG16 Architecture:** Simonyan & Zisserman ([Paper](https://arxiv.org/abs/1409.1556))
- **Grad-CAM:** Selvaraju et al. ([Paper](https://arxiv.org/abs/1610.02391))
- **Google Gemini AI:** Treatment recommendation generation
- **PyTorch Community:** Deep learning framework
- **Gradio:** Web interface framework

---

## 🌟 Star History

If this project helped you, please consider giving it a ⭐ on [GitHub](https://github.com/YOUR_USERNAME/fish-disease-detection)!

---

**Made with ❤️ for aquaculture health**