Optimizing Inception-V3 for Brain Tumor Classification Using Hybrid Precision Training and Cosine Annealing Learning Rate

In the realm of computational medical imaging, the precise classification of brain tumors from magnetic resonance (MR) images represents a quintessential challenge, demanding high fidelity in detection and categorization due to its critical implications for treatment planning and patient prognosis. This study pioneers the refinement of the Inception architecture, leveraging hybrid precision training and cosine annealing learning rate adjustment to enhance its computational efficiency and diagnostic accuracy in brain tumor classification tasks. Through a rigorous experimental framework, the modified Inception model exhibited exemplary performance, achieving an overall accuracy of 96.94% on a diverse test set, with precision metrics peaking at 99.32% for specific tumor categories. These results not only underscore the model's robust generalization capabilities but also its potential to set new standards in the automated interpretation of medical images. The comparative analysis conducted against established convolutional neural networks (CNNs) like AlexNet, VGG, and ResNet further corroborates the superiority of the proposed model, thereby cementing its status as a pivotal contribution to the field.