Skin_Diseases_Detection

Skin Diseases Detection Model (DermaDL)

Overview

DermaDL is a deep learning–based skin disease detection model trained on dermoscopic images.
It uses a Convolutional Neural Network (CNN) to classify multiple skin conditions and is intended for research, educational, and prototype applications.

⚠️ Disclaimer: This model is NOT a medical diagnostic tool and should not be used as a substitute for professional medical advice.

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Model Details

• Task: Image Classification
• Architecture: CNN with Transfer Learning
• Framework: PyTorch
• Input Size: 224 × 224 RGB images
• Accuracy: ~91% (validation set)

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Supported Skin Conditions

• Melanoma
• Basal Cell Carcinoma
• Squamous Cell Carcinoma
• Benign Keratosis
• Actinic Keratosis
• Dermatofibroma
• Vascular Lesions
• Nevus

(Class labels may vary depending on dataset version)

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Training Information

• Dataset: Public dermoscopic skin lesion datasets
• Loss Function: Cross-Entropy Loss
• Optimizer: Adam
• Evaluation Metric: Accuracy
• Data Augmentation: Rotation, flipping, normalization

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How to Use

1️⃣ Install Dependencies

pip install torch torchvision pillow