| Summary: | Brain tumor classification is important for diagnosing and treating cancers. Deep Learning has improved medical imaging with Artificial Intelligence (AI). Brain tumor's shape, size, and intensity make subclassification difficult. Medical imaging data is scarce. Any medical data involves privacy of the patients, hence unlike other image data, medical image data is not easily available. There are only few medical image data that is freely available for researchers. This project aims to develop a deep transfer learning model that can accurately classify brain cancers utilizing limited Medical Resonance Images (MRI) images. To achieve the goal, a modified GoogleNet model was used. Various learning algorithms were tested. The experiment also examined transfer learning and data augmentation. Finally, F1-average and confusion matrix were used to evaluate the model. Our model outperformed the state-of-the-art model in various research articles, according to performance matrices. Experimenters employed data augmentation and learning algorithms.
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