IMPLEMENTASI MASK-RCNN PADA DATASET KECIL CITRA SEL DARAH MERAH BERDASARKAN KRITERIA WARNA SEL
Examination of red blood cell morphology is one of the diagnostic aids for several diseases, one of which is anemia. The development of the application of digital image processing technology, artificial intelligence, and computer-assisted diagnosis opens opportunities to solve various problems related to medical images. Red blood cells sticking together or overlapping is a challenge in the red blood cell segmentation process which ultimately affects the results of cell type identification. A method that can perform instance segmentation is needed to overcome this problem. This study aims to implement the Mask-RCNN algorithm on a small red blood cell image dataset and evaluate the prediction results' performance. Based on the research results, the attached red blood cells can be detected individually by the model, and the accuracy of the cell detection results is 68.27%. Mask-RCNN can be used for blood cell segmentation instances and blood cell detection on small datasets, but the model accuracy still needs to be improved. Therefore it is necessary to do further research by increasing the number of datasets used.
L. Palmer et al., “ICSH recommendations for the standardization of nomenclature and grading of peripheral blood cell morphological features,” Int J Lab Hematol, vol. 37, no. 3, pp. 287–303, Jun. 2015, doi: 10.1111/ijlh.12327.
M. v Bills, B. T. Nguyen, and J.-Y. Yoon, “Simplified White Blood Cell Differential: An Inexpensive, Smartphone- and Paper-Based Blood Cell Count,” IEEE Sens J, vol. 19, no. 18, pp. 7822–7828, 2019, doi: 10.1109/JSEN.2019.2920235.
D. A. Tyas, S. Hartati, A. Harjoko, and T. Ratnaningsih, “Morphological, Texture, and Color Feature Analysis for Erythrocyte Classification in Thalassemia Cases,” IEEE Access, vol. 8, pp. 69849–69860, 2020, doi: 10.1109/ACCESS.2020.2983155.
H. Li, X. Zhao, A. Su, H. Zhang, J. Liu, and G. Gu, “Color Space Transformation and Multi-Class Weighted Loss for Adhesive White Blood Cell Segmentation,” IEEE Access, vol. 8, pp. 24808–24818, 2020, doi: 10.1109/ACCESS.2020.2970485.
H. H. Sultan, N. M. Salem, and W. Al-Atabany, “Multi-Classification of Brain Tumor Images Using Deep Neural Network,” IEEE Access, vol. 7, pp. 69215–69225, 2019, doi: 10.1109/ACCESS.2019.2919122.
C. Ge, I. Y.-H. Gu, A. S. Jakola, and J. Yang, “Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification,” IEEE Access, vol. 8, pp. 22560–22570, 2020, doi: 10.1109/ACCESS.2020.2969805.
S. Ahmad and P. K. Choudhury, “On the Performance of Deep Transfer Learning Networks for Brain Tumor Detection Using MR Images,” IEEE Access, vol. 10, pp. 59099–59114, 2022, doi: 10.1109/ACCESS.2022.3179376.
K. T. Navya, K. Prasad, and B. M. K. Singh, “Classification of blood cells into white blood cells and red blood cells from blood smear images using machine learning techniques,” in 2021 2nd Global Conference for Advancement in Technology (GCAT), 2021, pp. 1–4. doi: 10.1109/GCAT52182.2021.9587524.
K. He, G. Gkioxari, P. Dollar, and R. Girshick, “Mask R-CNN,” in 2017 IEEE International Conference on Computer Vision (ICCV), Oct. 2017, pp. 2980–2988. doi: 10.1109/ICCV.2017.322.
S. M. Abas, A. M. Abdulazeez, and D. Q. Zeebaree, “A YOLO and convolutional neural network for the detection and classification of leukocytes in leukemia,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 25, no. 1, p. 200, Jan. 2022, doi: 10.11591/ijeecs.v25.i1.pp200-213.
D. I. Saphietra, “Klasifikasi Sel Darah Merah Untuk Skrining Thalasemia Minor Menggunakan Transfer Learning Convolutional Neural Network,” Skripsi, UGM, Yogyakarta, 2021.
D. A. Tyas and T. Ratnaningsih, “Analisis Segmentasi Sel Darah Merah berbasis Mask-RCNN,” Journal of Informatics Information System Software Engineering and Applications (INISTA), vol. 5, no. 1, pp. 1–7, Nov. 2022, doi: 10.20895/inista.v5i1.766.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Please find the rights and licenses in RABIT : Jurnal Teknologi dan Sistem Informasi Univrab. By submitting the article/manuscript of the article, the author(s) accept this policy.
The non-commercial use of the article will be governed by the Creative Commons Attribution license as currently displayed on Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
2. Author’s Warranties
The author warrants that the article is original, written by stated author(s), has not been published before, contains no unlawful statements, does not infringe the rights of others, is subject to copyright that is vested exclusively in the author and free of any third party rights, and that any necessary written permissions to quote from other sources have been obtained by the author(s).
3. User Rights
RABIT's spirit is to disseminate articles published are as free as possible. Under the Creative Commons license, RABIT permits users to copy, distribute, display, and perform the work for non-commercial purposes only. Users will also need to attribute authors and RABIT on distributing works in the journal.
4. Rights of Authors
Authors retain all their rights to the published works, such as (but not limited to) the following rights;
- Copyright and other proprietary rights relating to the article, such as patent rights,
- The right to use the substance of the article in own future works, including lectures and books,
- The right to reproduce the article for own purposes,
- The right to self-archive the article,
- The right to enter into separate, additional contractual arrangements for the non-exclusive distribution of the article's published version (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal (RABIT : Jurnal Teknologi dan Sistem Informasi Univrab).
If the article was jointly prepared by other authors, any authors submitting the manuscript warrants that he/she has been authorized by all co-authors to be agreed on this copyright and license notice (agreement) on their behalf, and agrees to inform his/her co-authors of the terms of this policy. RABIT will not be held liable for anything that may arise due to the author(s) internal dispute. RABIT will only communicate with the corresponding author.
This agreement entitles the author to no royalties or other fees. To such extent as legally permissible, the author waives his or her right to collect royalties relative to the article in respect of any use of the article by RABIT.
RABIT will publish the article (or have it published) in the journal if the article’s editorial process is successfully completed. RABIT's editors may modify the article to a style of punctuation, spelling, capitalization, referencing and usage that deems appropriate. The author acknowledges that the article may be published so that it will be publicly accessible and such access will be free of charge for the readers as mentioned in point 3.