Retinal Optical Coherence Tomography (OCT) Image Classification for Glaucoma Detection

In our upcoming work, we plan to develop a robust deep learning model for Retinal OCT images. We will start by curating a diverse dataset and then preprocess it by resizing, normalizing, and applying data augmentation. Our model will be built on convolutional neural networks, possibly leveraging transfer learning. To enhance robustness, we'll incorporate techniques like dropout and batch normalization and fine-tune hyperparameters through cross-validation. We'll assess model performance using accuracy, sensitivity, and specificity metrics, and explore interpretability tools like Grad-CAM. Continuous monitoring and updates will be essential for adapting the model to evolving challenges and new data, ensuring it remains effective in diagnosing retinal conditions from OCT images.