This repository contains the code for a deep learning model developed for healthcare diagnosis. The model is trained to classify skin lesion images into different categories for disease diagnosis.
The dataset consists of skin lesion images categorized into different classes representing various skin diseases. The images are preprocessed and split into training, validation, and test sets.
The model architecture is built using the TensorFlow backend with the Keras library. It includes convolutional layers followed by max-pooling layers and dropout layers to prevent overfitting. The final layer uses a softmax activation function for multi-class classification.
The model is trained using stochastic gradient descent (SGD) optimizer and sparse categorical cross-entropy loss function. Training is performed for a specified number of epochs with a defined batch size. Validation data is used to monitor the model's performance during training.
The trained model is evaluated on a separate test set to assess its performance on unseen data. Evaluation metrics such as loss and accuracy are computed and visualized using plots.
To use this model:
- Clone this repository.
- Ensure you have the required dependencies installed.
- Run the provided notebook
AIHEALTHCARE.ipynb. - Follow the instructions within the notebook to load the data, build, train, and evaluate the model.
- TensorFlow
- Keras
- Matplotlib
- Other dependencies specified in the notebook
This project is licensed under the MIT License. See the LICENSE file for details.
For any inquiries or suggestions, please feel free to contact the author:
Tuncay Özalıcı - tuncay.ozalici@gmail.com - GitHub - LinkedIn