Classification of Brain Tumors using Hybrid Deep Learning Models

TL;DR

This paper compares deep learning models for brain tumor classification, demonstrating that EfficientNetV2 outperforms others in accuracy but requires more training time, highlighting trade-offs in model complexity.

Contribution

The study introduces transfer learning with EfficientNetV2 for brain tumor classification, showing its superior performance over EfficientNet and ResNet50.

Findings

EfficientNetV2 achieved higher classification accuracy.

EfficientNetV2 required more training time.

Transfer learning enabled effective classification with fewer samples.

Abstract

The use of Convolutional Neural Networks (CNNs) has greatly improved the interpretation of medical images. However, conventional CNNs typically demand extensive computational resources and large training datasets. To address these limitations, this study applied transfer learning to achieve strong classification performance using fewer training samples. Specifically, the study compared EfficientNetV2 with its predecessor, EfficientNet, and with ResNet50 in classifying brain tumors into three types: glioma, meningioma, and pituitary tumors. Results showed that EfficientNetV2 delivered superior performance compared to the other models. However, this improvement came at the cost of increased training time, likely due to the model's greater complexity.