This project utilizes deep learning techniques, specifically the cnn and the VGG16 convolutional neural network, to detect pneumonia from chest X-ray images. The models are trained on a dataset consisting of images labeled as either "normal" or "pneumonia".
The VGG16 model, pre-trained on ImageNet, is employed as the base model. The top layers of VGG16 are adapted to fit the binary classification task of pneumonia detection. The model is fine-tuned by freezing the convolutional base and training only the custom classifier layers.
The model is compiled with:
1-Loss Function: Categorical Cross-Entropy, suitable for multi-class classification.
2-Optimizer: Adam optimizer for efficient weight updates.
3-Metrics: Accuracy metric to monitor model performance during training.
The training process involves iterating through batches of augmented images generated by ImageDataGenerator, optimizing the model parameters to minimize the loss function.
Once trained, the model can make predictions on new chest X-ray images to classify them as either normal or indicating pneumonia. The predictions are based on the highest probability output from the softmax layer of the model.
Ensure you have the following dependencies installed:
-Keras
-TensorFlow
-Matplotlib
-NumPy
-Incorporate more advanced architectures like ResNet or DenseNet for potentially improved performance.
-Experiment with different augmentation techniques to further enhance model generalization.