EfficientNet FPN-Based Brain Tumor Segmentation.

EasyChair Preprint 10202, version 2

Abstract

Brain tumors are complex and dangerous conditions that require accurate diagnosis for effective treatment. While MRI is a crucial diagnostic tool, the process of interpreting and evaluating MRI is time-consuming and requires expertise. Developing AI and machine learning methods to predict brain tumors can speed up diagnosis, reduce wait times, and improve accuracy. In this study, the authors validated and used the EfficientNet model combined with FPN to segment brain tumors in reality. We trained model on the BraTS 2020 dataset, achieving good performance on the test and evaluation sets. The proposed method demonstrated an average IoU accuracy of 0.9083 and 0.8878 and an average Dice accuracy of 0.9336 and 0.9303 on the test and evaluation sets, respectively.

Keyphrases: BRATS 2020, Brain Tumor, EfficientNet model, FPN

@booklet{EasyChair:10202,
  author    = {Van Kiet Vo and Phuoc Huy Tran},
  title     = {EfficientNet FPN-Based Brain Tumor Segmentation.},
  howpublished = {EasyChair Preprint 10202},
  year      = {EasyChair, 2023}}