🧬 SmartSkin AI — Deep Learning Skin Disease Classifier

AI-powered skin disease classification system built using Deep Learning and Computer Vision.

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📌 Problem

Skin disease diagnosis often requires expert medical consultation and manual visual analysis, which may not always be easily accessible.

Traditional screening methods can be:

• Time-consuming
• Dependent on specialist availability
• Difficult to access in remote areas

There is a growing need for lightweight AI systems that can assist in preliminary image-based skin disease analysis.

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💡 Solution

SmartSkin AI is a Deep Learning-based Computer Vision system that classifies skin disease images using Transfer Learning.

The system:

• Accepts skin images as input
• Processes images using Computer Vision techniques
• Uses a MobileNetV2 Deep Learning model for classification
• Generates prediction results through a Streamlit-based interface

The project demonstrates how AI can support healthcare-focused image analysis workflows.

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🚀 Features

• Skin disease image classification
• MobileNetV2 Transfer Learning
• Real-time prediction workflow
• Streamlit interactive UI
• Lightweight deployment-ready architecture
• Medical Computer Vision implementation

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🧠 Model Information

Component	Details
Model Architecture	MobileNetV2
Framework	TensorFlow / Keras
Task	Multi-class Classification
Domain	Medical Computer Vision
Input Type	Skin Images
Deployment Interface	Streamlit

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⚙️ Tech Stack

AI / Deep Learning

• Python
• TensorFlow
• Keras
• OpenCV
• NumPy

Deployment / Interface

• Streamlit

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📂 Project Structure

SmartSkin-AI-Deep-Learning/
│
├── dataset/
├── model/
├── notebooks/
├── app.py
├── requirements.txt
└── README.md

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🖼️ Workflow

Image Input
   ↓
Preprocessing
   ↓
Feature Extraction
   ↓
Disease Classification
   ↓
Prediction Output

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▶️ Installation

Clone Repository

git clone https://github.com/Gourav-512/SmartSkin-AI-Deep-Learning.git

cd SmartSkin-AI-Deep-Learning

Install Dependencies

pip install -r requirements.txt

Run Application

streamlit run app.py

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🌐 Deployment

Local Deployment

Run using Streamlit locally for real-time prediction.

Possible Cloud Deployment Platforms

• Hugging Face Spaces
• Streamlit Cloud
• Render
• AWS
• Railway

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📊 Results

• Successfully implemented MobileNetV2-based image classification
• Achieved real-time prediction workflow
• Built lightweight medical AI inference pipeline
• Developed interactive deployment-ready UI
• Demonstrated practical use of Transfer Learning in healthcare AI

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📚 Learning Outcomes

This project helped in understanding:

• Transfer Learning workflows
• Medical image classification
• CNN-based Deep Learning pipelines
• TensorFlow model deployment
• Streamlit AI applications
• Computer Vision preprocessing techniques

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🔥 Future Improvements

• Improve model accuracy
• Expand disease categories
• Add Explainable AI visualizations
• Deploy production-ready API backend
• Add cloud inference support
• Optimize mobile deployment

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👨‍💻 Author

Gourav Salunkhe

Applied AI Engineer focused on:

• Computer Vision
• Deep Learning
• AI Deployment
• MLOps Fundamentals

🔗 GitHub: https://github.com/Gourav-512

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⭐ Support

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• Star the repository
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