Classification of some epidemics through microscopic images by using deep learning. Comparison
Vol 6, Issue 1, 2023
VIEWS - 726 (Abstract) 52 (PDF)
Abstract
In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.
Keywords
Full Text:
PDFReferences
1. Moorthy J, Gandhi UD. A Survey on Medical Image Segmentation Based on Deep Learning Techniques. Big Data and Cognitive Computing. 2022; 6(4): 117. doi: 10.3390/bdcc6040117
2. Kaur A, Singh Y, Neeru N, et al. A Survey on Deep Learning Approaches to Medical Images and a Systematic Look up into Real-Time Object Detection. Archives of Computational Methods in Engineering. 2021; 29(4): 2071-2111. doi: 10.1007/s11831-021-09649-9
3. Alzubaidi L, Zhang J, Humaidi AJ, et al. Review of deep learning: concepts, CNN architectures, challenges, applications, future directions. Journal of Big Data. 2021; 8(1). doi: 10.1186/s40537-021-00444-8
4. Rodríguez R, Mondeja BA, Valdés O, et al. SARS-CoV-2: enhancement and segmentation of high-resolution microscopy images—Part I. Signal, Image and Video Processing. 2021; 15(8): 1713-1721. doi: 10.1007/s11760-021-01912-7
5. Rodríguez R, Mondeja BA, Valdes O, et al. SARS-CoV-2: theoretical analysis of the proposed algorithms to the enhancement and segmentation of high-resolution microscopy images—Part II. Signal, Image and Video Processing. 2022; 16(3): 595-604. doi: 10.1007/s11760-021-02045-7
6. Basu A, Senapati P, Deb M, et al. A survey on recent trends in deep learning for nucleus segmentation from histopathology images. Evolving Systems. 2023; 15(1): 203-248. doi: 10.1007/s12530-023-09491-3
7. Yang T, Luo Y, Ji W, et al. Advancing biological super-resolution microscopy through deep learning: a brief review. Biophysics Reports. 2021; 7(4): 253. doi: 10.52601/bpr.2021.210019
8. Rodríguez R, Sossa JH. Mathematical Techniques for Biomedical Image Segmentation. Encyclopedia of Biomedical Engineering. Published online 2019: 64-78. doi: 10.1016/b978-0-12-801238-3.99989-6
9. Ledón T, et al. Vibrio cholerae O139: Emergence, evolution, and genetic structure of CTXΦ (Spanish). Revista CENIC Ciencias Biológicas. 2007; 38(1): 062-067.
10. Haldar K, Mohandas N. Malaria, erythrocytic infection, and anemia. Hematology. 2009; 2009(1): 87-93. doi: 10.1182/asheducation-2009.1.87
11. Yang W, Zhang X, Tian Y, et al. Deep Learning for Single Image Super-Resolution: A Brief Review. arXiv. 2019; arXiv:1808.03344v3.
12. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Communications of the ACM. 2017; 60(6): 84-90. doi: 10.1145/3065386
13. Borja-Robalino R, Monleón-Getino A, Rodellar J. Standardization of performance metrics for classifiers (Spanish). Revista Ibérica de Sistemas e Tecnologias de Informação. 2020; E30: 184-196.
14. Traore BB, Kamsu-Foguem B, Tangara F. Deep convolution neural network for image recognition. Ecological Informatics. 2018; 48: 257-268. doi: 10.1016/j.ecoinf.2018.10.002
15. Karim A, Singh J, Mishra A, et al. Toxicity prediction by multimodal deep learning. Pacific Rim Knowledge Acquisition Workshop. 2019; 2: 142-152.
16. Available online: http://www.evanlray.com/stat344ne_s2020/materials/20200226_generators_data_augmentation/20200225_stuff/20200225_stuff.pdf 2020 (accessed on 6 March 2023).
17. Ho Y, Wookey S. The Real-World-Weight Cross-Entropy Loss Function: Modeling the Costs of Mislabeling. IEEE Access. 2020; 8: 4806-4813. doi: 10.1109/access.2019.2962617
18. Javid AM, Das S, Skoglund M, et al. A ReLu dense layer to improve the performance of neural networks. arXiv. 2020; arXiv2010.13572v1.
19. Karim A, Mishra A, Newton MAH, et al. Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees. ACS Omega. 2019; 4(1): 1874-1888. doi: 10.1021/acsomega.8b03173
20. Margherita G, Enrico B, Giorgio V. Metrics for Multi-class Classification: An Overview. arXiv. 2020; arXiv:2008.05756v1.
21. Opitz J, Burst S. Macro F1 and Macro F. arXiv. 2021; arXiv:1911.03347v3.
22. Murphy KP. Machine learning: A probabilistic perspective. MIT Press; 2012.
DOI: https://doi.org/10.24294/irr.v6i1.5451
Refbacks
- There are currently no refbacks.
License URL: https://creativecommons.org/licenses/by-nc/4.0/
This site is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.