This project focuses on automating the detection of middle ear pathologies using the VGG16 convolutional neural network. Trained on a diverse dataset of annotated images from various imaging modalities, including otoscopy, CT, and MRI, the fine-tuned VGG16 model shows promising results. Its accuracy in identifying and classifying middle ear disorders is evaluated against traditional methods, showcasing advantages such as rapid image analysis and potential integration into healthcare systems. The VGG16-based approach holds promise for improving diagnostic accuracy, enabling early intervention, and enhancing patient outcomes in managing middle ear disorders. Further research is needed to validate its performance on larger datasets and explore integration into clinical practice.
