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

TL;DR

This paper introduces a transfer learning-based deep learning approach for brain tumor classification in MRI images, effectively addressing data imbalance issues and achieving high accuracy in distinguishing tumor types.

Contribution

It proposes a novel Transfer Learning-CNN model that combines pre-trained models with CNNs and evaluates strategies for handling data imbalance in brain tumor classification.

Findings

Achieved 96% accuracy in tumor classification.

Demonstrated the effectiveness of combining VGG-16 with CNN.

Showed that using focal loss and oversampling improves performance.

Abstract

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.