A Machine Learning model used to predict possible Skin Diseases This project aims to develop a machine-learning model for detecting various skin diseases using image analysis. The model is trained on a dataset of skin disease images and is capable of classifying new images into different disease categories.
Skin diseases are common and can vary widely in severity and symptoms. Early detection and diagnosis are crucial for effective treatment. This project leverages machine learning techniques to assist in the automated detection of skin diseases, which can help healthcare professionals in timely diagnosis.
The dataset used for training the model consists of images of various skin diseases, including but not limited to eczema, psoriasis, melanoma, and acne. The dataset is preprocessed and augmented to improve the model's performance and generalization.
The model architecture used for this project is based on a convolutional neural network (CNN). CNNs are well-suited for image classification tasks due to their ability to learn hierarchical features from images. Transfer learning is employed using a pre-trained CNN model to leverage features learned from a large dataset.
- Improve model performance with additional data and fine-tuning.
- Develop a web or mobile application for easy access to the model.
- Incorporate real-time image analysis for faster diagnosis.
Contributions to this project are welcome. Please fork the repository, make your changes, and submit a pull request.
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