Optimizing Brain Tumor Classification: A Comprehensive Study on Transfer
  Learning and Imbalance Handling in Deep Learning Models
Raza Imam Mohammed Talha Alam
Deep learning has emerged as a prominent field in recent literature,
showcasing the introduction of models that utilize transfer learning to achieve
remarkable 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 the
data imbalance but also prioritize precise classification of brain cancer. In
this work, we present a novel deep learning-based approach, called Transfer
Learning-CNN, for brain tumor classification using MRI data. The proposed model
leverages the predictive capabilities of existing publicly available models by
utilizing their pre-trained weights and transferring those weights to the CNN.
By leveraging a publicly available Brain MRI dataset, the experiment evaluated
various transfer learning models for classifying different tumor types,
including meningioma, glioma, and pituitary tumors. We investigate the impact
of different loss functions, including focal loss, and oversampling methods,
such as SMOTE and ADASYN, in addressing the data imbalance issue. Notably, the
proposed strategy, which combines VGG-16 and CNN, achieved an impressive
accuracy rate of 96%, surpassing alternative approaches significantly.