🩺 Skin Disease Detection Model

An application for detecting skin diseases using advanced Vision Transformer models. The model is quantized with ONNX for efficient performance and is served via a FastAPI backend.

📂 Dataset Used

The dataset used for this project is publicly available on Kaggle:
Skin Disease Dataset

This dataset consists of images classified into 22 distinct skin disease categories.

🧠 Model Information

Model Architecture: Vision Transformer (ViT)
Model Optimization: Quantized using ONNX for faster inference and reduced resource usage.

🩹 Supported Skin Disease Classes

The model can detect and classify the following skin conditions:

Acne
Actinic Keratosis
Benign Tumors
Bullous
Candidiasis
Drug Eruption
Eczema
Infestations/Bites
Lichen
Lupus
Moles
Psoriasis
Rosacea
Seborrheic Keratoses
Skin Cancer
Sun/Sunlight Damage
Tinea
Unknown/Normal
Vascular Tumors
Vasculitis
Vitiligo
Warts

⚙️ Backend Details

The backend is built with FastAPI, enabling seamless API interactions.

API Hosted Link:

Skin Disease Detect API Documentation

🛠️ Local Setup

Prerequisites

Ensure you have Python and pip installed. Then install the required packages:

pip install -r requirements.txt

🛠️ Running the Application

To start the application locally, run the following command:

gunicorn service.main:app --workers 2 --worker-class uvicorn.workers.UvicornWorker

✍️ Owner Information

Developed and maintained by Prashant Kumar Mishra.

GitHub: pacificrm
LinkedIn: Profile Link
For queries, suggestions, or contributions, feel free to reach out!

pacificrm/skinDiseasesDetection