Nervous System Based Gliomas Detection Based on Deep Learning Architecture in Segmentation

Authors

  • Dr. Gaurav Pathak Auckland University of Technology, Auckland New Zealand
  • Dr. Mohit Angurala Assistant Professor & Head of the department, Department of computer science and Engineering, Khalsa College of Engineering and Technology, Amritsar, Punjab, India https://orcid.org/0000-0002-9506-5864
  • Dr. Manju Bala Director, khalsa college of Engineering and Technology, Amritsar https://orcid.org/0000-0002-2313-0284

Keywords:

Gliomas, CNS, LGGs, HGGs, U-VGG-19 Net_ CNN

Abstract

The majority of malignant brain tumours are gliomas, a kind of central nervous system (CNS) tumour. This paper proposes segmentation based Gliomas analysis from nervous system using U- VGG-19 Net CNN architecture. Here by segmenting the tumor by neural network based technique, the HGGs will be detected at the earliest. We compared the models' averaged accuracy, precision, recall, and F1-score values, which were 98%, 96%, and 86.2% respectively. The classification of gliomas into LGG and HGG using the proposed custom model has demonstrated effectiveness and robustness in the results.

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Published

2020-12-13

How to Cite

Pathak, D. G., Angurala, D. M., & Bala, D. M. (2020). Nervous System Based Gliomas Detection Based on Deep Learning Architecture in Segmentation. Research Journal of Computer Systems and Engineering, 1(2), 01–06. Retrieved from https://vit.technicaljournals.org/index.php/rjcse/article/view/65

Issue

Volume 1 Issue 2 - 2020 (July to December)

Section

Articles