The Lung Disease Classifier is a project aimed at leveraging machine learning and deep learning techniques to classify lung diseases from X-ray images. By employing various preprocessing techniques, including filtration for noise removal, this classifier enhances the quality of input images for accurate predictions.
Key Features:
Preprocessing: Utilizes advanced filtration methods for noise removal, ensuring cleaner X-ray images for improved classification accuracy. Machine Learning Models: Implements a range of machine learning algorithms to analyze X-ray images and classify lung diseases with precision. Deep Learning Models: Harnesses the power of deep learning techniques to train neural networks for robust and efficient disease classification. Evaluation Metrics: Utilizes appropriate evaluation metrics to assess the performance of classification models and optimize predictive accuracy. User Interface: Provides a user-friendly interface for seamless interaction and easy access to classification results.
How to Use:
- Preprocessing: Apply noise removal techniques to input X-ray images using the provided filtration methods.
- Model Selection: Choose from a selection of machine learning and deep learning models for disease classification.
- Training: Train the selected model using labeled X-ray image datasets to learn disease patterns and features.
- Evaluation: Evaluate the performance of the trained model using appropriate evaluation metrics to ensure optimal classification accuracy.
- Prediction: Utilize the trained model to predict lung diseases from new X-ray images with confidence.
Dependencies:
Python 3.x TensorFlow Keras Scikit-learn OpenCV NumPy Matplotlib