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Deep learning has emerged as a prominent field in recent literature,showcasing the introduction of models that utilize transfer learning to achieveremarkable accuracies in the classification of brain tumor MRI images. However,the majority of these proposals primarily focus on balanced datasets,neglecting the inherent data imbalance present in real-world scenarios.Consequently, there is a pressing need for approaches that not only address thedata imbalance but also prioritize precise classification of brain cancer. Inthis work, we present a novel deep learning-based approach, called TransferLearning-CNN, for brain tumor classification using MRI data. The proposed modelleverages the predictive capabilities of existing publicly available models byutilizing their pre-trained weights and transferring those weights to the CNN.By leveraging a publicly available Brain MRI dataset, the experiment evaluatedvarious transfer learning models for classifying different tumor types,including meningioma, glioma, and pituitary tumors. We investigate the impactof different loss functions, including focal loss, and oversampling methods,such as SMOTE and ADASYN, in addressing the data imbalance issue. Notably, theproposed strategy, which combines VGG-16 and CNN, achieved an impressiveaccuracy rate of 96%, surpassing alternative approaches significantly.

Optimizing Brain Tumor Classification: A Comprehensive Study on Transfer Learning and Imbalance Handling in Deep Learning Models