International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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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)
  • Received : 2022.03.05
  • Published : 2022.03.30
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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.

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References

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