HYBRID LEARNING STRATEGY COMBINING MODEL-LEVEL TRANSFER LEARNING AND DATA-LEVEL AUGMENTATION FOR BRAIN CANCER CLASSIFICATION

Due to the complexity of images, size, and balance of data, brain cancer diagnosis is still one of the most challenging problems to solve. It is shown that traditional classification methods based on 'first principles' do not produce ideal results, often due to different brain tumours. This research uses a hybrid model that leverages transfer learning with data augmentation and AI refinement to categorise three brain tumours: glioma, meningioma, and others. This research aims to improve the classification performance of brain cancer detection using this model. The methodology uses a framework created with a specific dataset, mixed data enhancement, and InceptionV3 model refinement to improve performance. With a validation accuracy of 0.95, the F1 scores for glioma, meningioma, and other brain tumours were 0.98, 0.95, and 0.92, respectively. This hybrid model achieves accuracy without complexity in design while addressing data scarcity and balance issues. The primary focus of this research was to create an effective and robust model for classifying brain cancers that is easy to use in low-resource clinical environments. The results demonstrate how deep learning can improve diagnostic precision and provide a scalable method for detecting brain cancer in the early stages of medical imaging