Fish Erythrocytes Nuclear Abnormalities Classification using Machine Learning
Resumo
The creation of automated systems capable of detecting anomalies in fish erythrocytes is an important concern in the area of marine biology. We investigate the possibility of using machine learning to classify images of abnormal and normal nuclei of fish erythrocytes, considering three abnormalities: nuclear bud, notched nuclei, and vacuole nuclei, among others. Random Forests were shown to have the highest AUC median in both sets, reaching AUC values of 0.896 and 0.959 for all sets of classes and the vacuole set, respectively, being able to correctly classify a high percentage of the bud and notched cells. However, when all classes are considered, the outcome is impressively better.
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K. Mimura, S. Minabe, K. Nakamura, K. Yasukawa, J. Ohta, and Y. Kato, “Automated detection of microfossil fish teeth from slide images using combined deep learning models,” Applied Computing and Geosciences, vol. 16, p. 100092, 2022. [Online]. Available: [link].
K. Bijari, G. Valera, H. López-Schier, and G. A. Ascoli, “Quantitative neuronal morphometry by supervised and unsupervised learning,” STAR Protocols, vol. 2, no. 4, p. 100867, 2021. [Online]. Available: [link].
C. S. Costa, E. C. Tetila, G. Astolfi, D. A. Sant’Ana, M. C. Brito Pache, A. B. Gonçalves, V. A. Garcia Zanoni, H. H. Picoli Nucci, O. Diemer, and H. Pistori, “A computer vision system for oocyte counting using images captured by smartphone,” Aquacultural Engineering, vol. 87, 2019.
J. d. S. Azevedo, E. d. S. Braga, and C. A. O. Ribeiro, “Nuclear abnormalities in erythrocytes and morphometric indexes in the catfish cathorops spixii (ariidae) from different sites on the southeastern brazilian coast,” Brazilian Journal of Oceanography, vol. 60, pp. 323–330, 2012.
M. Stankevičiūtė, T. Gomes, and J. A. C. González, “Nuclear abnormalities in mussel haemocytes and fish erythrocytes,” 2022.
J. M. Phillip, K.-S. Han, W.-C. Chen, D. Wirtz, and P.-H. Wu, “A robust unsupervised machine-learning method to quantify the morphological heterogeneity of cells and nuclei,” Nature protocols, vol. 16, no. 2, pp. 754–774, 2021.
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S. N. Talapatra, R. Chaudhuri, and S. Ghosh, “Cellprofiler and weka tools: image analysis for fish erythrocytes shape and machine learning model algorithm accuracy prediction of dataset,” World Scientific News, vol. 154, pp. 101–116, 2021.
L. Tao and G. Xu, “Color in machine vision and its application,” CHINESE SCIENCE BULLETIN, vol. 46, no. 17, pp. 1411–1421, SEP 2001.
R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-3, no. 6, pp. 610–621, 1973.
M.-K. Hu, “Visual pattern recognition by moment invariants,” IRE Transactions on Information Theory, vol. 8, no. 2, pp. 179–187, 1962.
T. Ojala, M. Pietikainen, and T. Maenpaa, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 7, pp. 971–987, 2002.
D. Gabor, “Theory of communication. part 1: The analysis of information,” Journal of the Institution of Electrical Engineers - Part III: Radio and Communication Engineering, vol. 93, pp. 429–441(12), November 1946.
N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05), vol. 1, 2005, pp. 886–893 vol. 1.
P. A. A. Resende and A. C. Drummond, “A survey of random forest based methods for intrusion detection systems,” ACM Comput. Surv., vol. 51, no. 3, May 2018.
B. Chakradhar, I. S. Rao, V. J. Archana, and C. V. K. Hari, “Detection of malignancy on dermis using j48 and random forest classifiers,” in 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA). IEEE, 2020, pp. 1–6.
J. Nalepa and M. Kawulok, “Selecting training sets for support vector machines: a review,” Artificial Intelligence Review, vol. 52, pp. 857–900, 2019.
N. Ali, D. Neagu, and P. Trundle, “Evaluation of k-nearest neighbour classifier performance for heterogeneous data sets,” SN Applied Sciences, vol. 1, no. 1559, 2019.
P. Grimaldi, M. Lorenzati, M. Ribodino, E. Signorino, A. Buffo, and P. Berchialla, “Predicting astrocytic nuclear morphology with machine learning: A tree ensemble classifier study,” Applied Sciences, vol. 13, no. 7, p. 4289, 2023.
F. J. Moreno-Barea, L. Franco, D. Elizondo, and M. Grootveld, “Application of data augmentation techniques towards metabolomics,” Computers in Biology and Medicine, vol. 148, p. 105916, 2022.
Y. Li, J. Gong, X. Shen, M. Li, H. Zhang, F. Feng, and T. Tong, “Assessment of primary colorectal cancer ct radiomics to predict metachronous liver metastasis,” Frontiers in Oncology, vol. 12, p. 861892, 2022.
B. Wang and C. Shi, “Shape matching using chord-length function,” in Intelligent Data Engineering and Automated Learning – IDEAL 2006, E. Corchado, H. Yin, V. Botti, and C. Fyfe, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006, pp. 746–753.
Publicado
13/11/2023
Como Citar
LOEBENS, Newton et al.
Fish Erythrocytes Nuclear Abnormalities Classification using Machine Learning. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 18. , 2023, São Bernardo do Campo/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
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p. 96-101.
DOI: https://doi.org/10.5753/wvc.2023.27539.
