DermaV1 is a machine learning project developed to detect and classify common skin diseases from images. Trained using a curated dataset of skin conditions, the model takes an image input and predicts the type of skin disease, helping to demonstrate how AI can support early medical diagnosis and accessibility.
- 📸 Image-based skin disease recognition
- 🧠 Built with supervised ML techniques
- 🧪 Includes full pipeline: preprocessing → training → evaluation
- 🖼️ Web scraping used to collect training data
- 📊 Exploratory data analysis included
- 📂 Modular code structure for easy scaling
| File | Description |
|---|---|
data_preprocessing.py |
Prepares and augments image data for training |
data_analysis.py |
Performs EDA (Exploratory Data Analysis) on dataset |
model_building.py |
Builds the machine learning model architecture |
model_training.py |
Trains the model on the dataset |
model_evaluation.py |
Evaluates accuracy, precision, and other metrics |
web scrapper.py |
Collects images from the web for training data |
main.py |
Runs the complete inference pipeline |
imagefodify.py / imgflipper.py |
Data augmentation scripts |
LICENSE |
MIT License |
- Type: Image Classification Model
- Framework:
scikit-learn,OpenCV, andmatplotlib - Language: Python
- Input: JPG/PNG images of skin conditions
- Output: Predicted class of skin disease
python main.py --image path/to/test_image.jpg