Two-headed UNetEfficientNets for parallel execution of segmentation and classification of brain tumors: incorporating postprocessing techniques with connected component labelling.

Journal: Journal of cancer research and clinical oncology
PMID: 38684578

Abstract

PURPOSE: The purpose of this study is to develop accurate and automated detection and segmentation methods for brain tumors, given their significant fatality rates, with aggressive malignant tumors like Glioblastoma Multiforme (GBM) having a five-year survival rate as low as 5 to 10%. This underscores the urgent need to improve diagnosis and treatment outcomes through innovative approaches in medical imaging and deep learning techniques.

Authors

  • Hari Mohan Rai
    School of Computing, Gachon University, 1342 Seongnam-daero, Sujeong-Gu, Seongnam-Si, 13120, Gyeonggi-Do, Republic of Korea. drhmrai@gachon.ac.kr.
  • Joon Yoo
    School of Computing, Gachon University, 1342 Seongnamdaero, Seongnam, 13120, South Korea.
  • Serhii Dashkevych
    Department of Computer Engineering, Vistula University, Stokłosy 3, 02-787, Warszawa, Poland.

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