metadata
license: mit
library_name: pytorch
pipeline_tag: image-classification
tags:
- skin-disease
- medical-imaging
- cnn
- deep-learning
- healthcare
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.
Model Details
- Task: Image Classification
- Architecture: CNN with Transfer Learning
- Framework: PyTorch
- Input Size: 224 × 224 RGB images
- Accuracy: ~91% (validation set)
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)
Training Information
- Dataset: Public dermoscopic skin lesion datasets
- Loss Function: Cross-Entropy Loss
- Optimizer: Adam
- Evaluation Metric: Accuracy
- Data Augmentation: Rotation, flipping, normalization
How to Use
1️⃣ Install Dependencies
pip install torch torchvision pillow