[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.22937/IJCSNS.2022.22.3.35

Comparing U-Net convolutional network with mask R-CNN in Nuclei Segmentation

Zanaty, E.A. (Mathematics and Computer Science Department, Faculty of Science, Sohag University)
Abdel-Aty, Mahmoud M. (Mathematics and Computer Science Department, Faculty of Science, Sohag University)
ali, Khalid abdel-wahab (Mathematics and Computer Science Department, Faculty of Science, Sohag University)

Publication Information

International Journal of Computer Science & Network Security / v.22, no.3, 2022 , pp. 273-275 More about this Journal

Abstract

Deep Learning is used nowadays in Nuclei segmentation. While recent developments in theory and open-source software have made these tools easier to implement, expert knowledge is still required to choose the exemplary model architecture and training setup. We compare two popular segmentation frameworks, U-Net and Mask-RCNN, in the nuclei segmentation task and find that they have different strengths and failures. we compared both models aiming for the best nuclei segmentation performance. Experimental Results of Nuclei Medical Images Segmentation using U-NET algorithm Outperform Mask R-CNN Algorithm.

Keywords

U-Net; Mask R-CNN; Nuclei Segmentation;

Citations & Related Records

Reference

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