EfficientNet for Brain-Lesion classification

TL;DR

This paper applies EfficientNet B0 and Multiscale-EfficientNet architectures to classify brain lesions in 3D MRI images, achieving competitive results and advancing automated diagnosis methods.

Contribution

The paper introduces the use of EfficientNet B0 and Multiscale-EfficientNet for brain-lesion classification in 3D MRI data, a novel application of these architectures.

Findings

EfficientNet B0 achieves competitive classification scores.

Multiscale-EfficientNet improves slice-based MRI classification.

Proposed methods enhance early diagnosis potential.

Abstract

In the development of technology, there are increasing cases of brain disease, there are more treatments proposed and achieved a positive result. However, with Brain-Lesion, the early diagnoses can improve the possibility for successful treatment and can help patients recuperate better. From this reason, Brain-Lesion is one of the controversial topics in medical images analysis nowadays. With the improvement of the architecture, there is a variety of methods that are proposed and achieve competitive scores. In this paper, we proposed a technique that uses efficient-net for 3D images, especially the Efficient-net B0 for Brain-Lesion classification task solution, and achieve the competitive score. Moreover, we also proposed the method to use Multiscale-EfficientNet to classify the slices of the MRI data