Skin Disease Classification Using Deep Learning

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🚀 Overview

This project presents a deep learning-based solution for Skin Disease Classification using Convolutional Neural Networks (CNN), specifically the DenseNet201 architecture. The model distinguishes between benign and malignant skin conditions, trained on the ISIC (International Skin Imaging Collaboration) dataset.

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📂 Dataset

The dataset consists of 2,357 dermoscopic images across the following 9 skin disease classes:

• Actinic Keratosis
• Basal Cell Carcinoma
• Dermatofibroma
• Melanoma
• Nevus
• Pigmented Benign Keratosis
• Seborrheic Keratosis
• Squamous Cell Carcinoma
• Vascular Lesion

Source: Kaggle - Skin Cancer 9 Classes (ISIC)

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📁 Directory Structure

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├── app.py                                # Streamlit web app for predictions
├── skin_disease_model.h5                 # Pretrained DenseNet201 model
├── requirements.txt                      # Dependency list
├── Dockerfile                            # Docker setup for deployment
├── README.md                             # Project documentation
└── skin-disease-my-own-prepared.ipynb    # Model training and evaluation notebook

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🐳 Docker Deployment

Run the application in a Docker container:

Step 1: Build the Docker image

docker build -t skin_disease_classification .

Step 2: Run the Docker container

docker run -p 8501:8501 skin_disease_classification

Visit http://localhost:8501 in your browser.

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🎯 Key Features

• User-friendly Streamlit interface for image upload and prediction
• Accurate classification using DenseNet201
• Fully containerized setup for portability and deployment ease

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📜 License

This project is licensed under the MIT License.

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🙌 Acknowledgments

• ISIC for providing the medical image dataset
• TensorFlow and Keras for deep learning support