Skin Disease Detection using CNN

This project uses a Convolutional Neural Network (CNN) built with TensorFlow and OpenCV to classify skin diseases from image datasets. The system performs image preprocessing, model training, and real-time prediction for disease classification.

Features

• Image preprocessing and augmentation
• CNN-based image classification
• TensorFlow model training
• Real-time prediction support
• OpenCV-based image handling

Tech Stack

• Python
• TensorFlow
• OpenCV
• NumPy
• Matplotlib

Model Workflow

1. Skin disease image dataset is collected and organized into labeled categories.
2. Images are preprocessed and resized using OpenCV.
3. Data augmentation techniques are applied to improve model generalization.
4. CNN model is trained using TensorFlow on the processed dataset.
5. Trained model performs disease classification on input images.
6. Prediction output is displayed for real-time image testing.

Training Performance

Prediction Output

Project Workflow

How to Run

1. Clone the repository bash git clone https://github.com/renganayagi2005/skin-disease-detection-cnn.git pip install tensorflow opencv-python numpy matplotlib jupyter notebook