Improved Global U-Net applied for multi-modal brain tumor fuzzy segmentation

Mishra, Annu; Gupta, Pankaj; Tewari, Peeyush

doi:10.47974/JIM-1767

Open Access Research Article

Improved Global U-Net applied for multi-modal brain tumor fuzzy segmentation

^*Annu MishraCorresponding authorannu.mishra2@sharda.ac.inDepartment of Computer Science and Engineering Birla Institute of Technology - MesraDepartment of Computer Science and Engineering Sharda School of Engineering and TechnologyGreater Noida, Uttar Pradesh, 201310, IndiaView full profile → , Pankaj Guptapgupta@bitmesra.ac.inDepartment of Computer Science and Engineering Birla Institute of Technology - MesraRanchi, Jharkhand, IndiaView full profile → , Peeyush Tewaripeeyush@bitmesra.ac.inDepatment of Mathematics Birla Institute of Technology - MesraJaipur, Rajasthan, IndiaView full profile →

* Corresponding author · click or hover a name for details

pp. 547–561Vol. 27Issue 3April 2024DOI: 10.47974/JIM-1767XML

Received:: 07 Nov 2023
Published Online:: 07 May 2024
Article type:: Research Article
Language:: EN
Article no.:: JIM-1767
Pages:: 547–561

Abstract

In this paper, we extended our work from Global U-Net combined with fuzzy amalgamation of Inception Model and Improved Kernel Variation for MRI Brain Image Segmentation [1] which was meant for single modality MRI images only to a brain tumor fuzzy segmentation. Many CNNs gives state of art results for a particular type of images. However, they cannot achieve the same result for the images captured from different imaging techniques. We experimented the Global U-Net model for MRI images earlier and this time we intended to make it applicable for other type of images too using the concept of fuzzy segmentation. The major concern was to overcome the limitations of single modality system that is not all the kernels of U-Net are capable of generating clear feature vectors for different image modalities. The result generated was satisfactory and we would further extend it for colored images.

Keywords

U-Net Multi-modality Fuzzy image segmentation Pooling layer Aggregation block

Subject Classifications

68U10

References

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