Red blood cells classification with sharpening segmentation and mask R-CNN

Arianti, Nunik Destria and Muda, Azah Kamilah and Ahmad, Norashikin (2023) Red blood cells classification with sharpening segmentation and mask R-CNN. In: 14th International Conference on Soft Computing and Pattern Recognition, SoCPaR 2022, and the 14th World Congress on Nature and Biologically Inspired Computing, NaBIC 2022, 14 December 2022through 16 December 2022, Virtual, Online.

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Abstract

Identifying and measuring red blood cells (RBC) before prescribing treatment for blood-related disorders is crucial for appropriate diagnosis. A pathologist manually performs such a process under a light microscope, as is customary. Nevertheless, manual visual inspection is arduous and subjective, leading to variance in RBC identification and counting. This paper proposes classification using sharpening segmentation combined with the algorithm mask R-CNN to increase the accuracy of calculating the number of RBCs. Eventually, the RBCs were classified as overlapping or single RBCs using the mask R-CNN classifier algorithm. In this study, a combination of 3 preprocessing image methods, including sharpening with the wand library, clahe, and the Otsu threshold, was used. The classification with the sharpening method proposed in this study gives an average accuracy of 17.4% better than without sharpening. The suggested approach has been evaluated on images of RBC and exhibits a reliable and effective methodology for classifying single and overlapping RBC.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Image processing, Overlapping, Red blood cell, Sharpening
Divisions:	Faculty Of Mechanical Technology And Engineering
Depositing User:	Maizatul Najwa Ahmad
Date Deposited:	20 Sep 2024 09:48
Last Modified:	20 Sep 2024 09:48
URI:	http://eprints.utem.edu.my/id/eprint/27878
Statistic Details:	View Download Statistic

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