Advancing Early-Stage Brain Tumor Detection with Segmentation by Modified_UNet

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Prompt and accurate detection of brain tumors is crucial to implement effective medical intervention, as brain tumors present a substantial health hazard. The goal of this work is to employ Magnetic Resonance Imaging (MRI) segmentation for brain tumor identification. A robust and efficient strategy is proposed to address the pressing need for more precise procedures in the diagnosis of brain tumors. The proposed technique makes use of the pre-trained UNet model backbone, which is intended to improve segmentation performance. The UNet encoder incorporates pre-trained models as backbone, thereby augmenting feature representation diversity and resulting in improved segmentation accuracy. Empirical evaluations of the dataset show promising results in brain tumor detection compared to existing approaches. An intersection over union (IoU) score of 0.8258 is achieved by this approach over a dataset of 6128 MRI slices with the threshold set to 0.9 which is relatively higher than UNet segmentation.

Original languageEnglish
Title of host publicationICMHI 2024 - 2024 8th International Conference on Medical and Health Informatics
PublisherAssociation for Computing Machinery
Pages52-57
Number of pages6
ISBN (Electronic)9798400716874
DOIs
Publication statusPublished - May 17 2024
Event8th International Conference on Medical and Health Informatics, ICMHI 2024 - Yokohama, Japan
Duration: May 17 2024May 19 2024

Publication series

NameACM International Conference Proceeding Series

Conference

Conference8th International Conference on Medical and Health Informatics, ICMHI 2024
Country/TerritoryJapan
CityYokohama
Period5/17/245/19/24

Keywords

  • Backbone model
  • early-stage brain tumor detection
  • Encoder-Decoder
  • feature extraction
  • ResNet
  • Segmentation
  • UNet

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

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