A ResNet based transfer learning pulmonary medical image disease detection system

Medical imaging represents a significant application of deep learning. In response to the vast domain of medical image analysis and the limitations and inefficiencies of conventional methods for diagnosing brain diseases, this paper explores the application of an enhanced ResNet-50 model in the classification of brain CT images. The goal is to improve the detection and classification accuracy of aneurysms, cancer, and malignant tumors. The study utilized 259 images, undergoing training, validation, and testing processes to verify the model's efficacy. The ResNet-50 model addresses the issue of vanishing gradients in deep networks through residual learning, making it suitable for high-resolution images and small datasets. Results indicate good training performance, though some fluctuations in validation performance, achieving a validation accuracy of 0.960. This research not only enhances diagnostic accuracy for brain diseases but also paves a new path for the further application of deep learning technologies in the medical field.