An Expert System for Leukocyte Classification using Probabilistic Deep Feature Optimization via Distribution Estimation
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References
- Abdullah, E. and Turan, M.K. (2019). Classifying white blood cells using machine learning algorithms,
International Journal of Engineering Research and Development 11 (1): 141–152. - Ahmad, R., Awais, M., Kausar, N. and Akram, T. (2023a). White blood cells classification using entropy-controlled deep features optimization,
Diagnostics 13 (3): 352–369. - Ahmad, R., Awais, M., Kausar, N., Tariq, U., Cha, J.-H. and Balili, J. (2023b). Leukocytes classification for leukemia detection using quantum inspired deep feature selection,
Cancers 15 (9): 2507–2524. - Almezhghwi, K. and Serte, S. (2020). Improved classification of white blood cells with the generative adversarial network and deep convolutional neural network,
Computational Intelligence and Neuroscience 2020 (01): 6490479. - Alruwaili, M. (2021). An intelligent medical imaging approach for various blood structure classifications,
Complexity 2021 (01): 5573300. - Bartlett, M.S. (1937). Properties of sufficiency and statistical tests,
Proceedings of the Royal Society of London A: Mathematical and Physical Sciences 160 (901): 268–282. - Farag, M.R. and Alagawany, M. (2018). Erythrocytes as a biological model for screening of xenobiotics toxicity,
Chemico-Biological Interactions 279 : 73–83. - Gupta, D., Agrawal, U., Arora, J. and Khanna, A. (2020). Bat-inspired algorithm for feature selection and white blood cell classification,
in X. Yang (Ed),Nature-Inspired Computation and Swarm Intelligence , Academic Press, Cambridge, pp. 179–197. - He, B., Lu, Q., Lang, J., Yu, H., Peng, C., Bing, P., Li, S., Zhou, Q., Liang, Y. and Tian, G. (2020). A new method for CTC images recognition based on machine learning,
Frontiers in Bioengineering and Biotechnology 8 : 897–907. - He, K., Zhang, X., Ren, S. and Sun, J. (2016). Deep residual learning for image recognition,
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVR), Las Vegas, USA , pp. 770–778. - Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M. and Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications,
arXiv: 1704.04861. - Huang, H., Wu, N., Liang, Y., Peng, X. and Shu, J. (2022). Slnl: A novel method for gene selection and phenotype classification,
International Journal of Intelligent Systems 37 (9): 6283–6304. - ImageNet (2024). ImageNet project image database,
http://www.image-net.org . - Iqbal, M., Naeem, M., Ahmed, A., Awais, M., Anpalagan, A. and Ahmad, A. (2018). Swarm intelligence based resource management for cooperative cognitive radio network in smart hospitals,
Wireless Personal Communications 98 : 571–592. - Jung, C., Abuhamad, M., Mohaisen, D., Han, K. and Nyang, D. (2022). WBC image classification and generative models based on convolutional neural network,
BMC Medical Imaging 22 (1): 1–16. - Kandukuri, U.R., Prakash, A.J., Patro, K.K., Neelapu, B.C., Tadeusiewicz, R. and Pławiak, P. (2023). Constant Q-transform-based deep learning architecture for detection of obstructive sleep apnea,
International Journal of Applied Mathematics and Computer Science 33 (3): 493–506, DOI: 10.34768/amcs-2023-0036. - Ko, B., Gim, J. and Nam, J. (2011). Cell image classification based on ensemble features and random forest,
Electronics Letters 47 (11): 638–639. - Kouzehkanan, Z.M., Saghari, S., Tavakoli, S., Rostami, P., Abaszadeh, M., Mirzadeh, F., Satlsar, E.S., Gheidishahran, M., Gorgi, F., Mohammadi, S. and Hosseini, R. (2022). A large dataset of white blood cells containing cell locations and types, along with segmented nuclei and cytoplasm,
Scientific Reports 12 (1): 1123–1137. - Liu, J., Lin, Y., Li, Y., Weng, W. and Wu, S. (2018). Online multi-label streaming feature selection based on neighborhood rough set,
Pattern Recognition 84 : 273–287. - Lu, S., Liu, S., Hou, P., Yang, B., Liu,M., Yin, L. and Zheng,W. (2023). Soft tissue feature tracking based on deep matching network,
Computer Modeling in Engineering and Sciences 136 (1): 363–379. - Malik, S., Akram, T., Awais, M., Khan, M.A., Hadjouni, M., Elmannai, H., Alasiry, A., Marzougui, M. and Tariq, U. (2023). An improved skin lesion boundary estimation for enhanced-intensity images using hybrid metaheuristics,
Diagnostics 13 (7): 1285. - Mathur, A., Tripathi, A.S. and Kuse,M. (2013). Scalable system for classification of white blood cells from Leishman stained blood stain images,
Journal of Pathology Informatics 4 (2): 15–20. - Redmon, J. and Farhadi, A. (2018). YOLOv3: An incremental improvement,
arXiv: 1804.02767. - Rezatofighi, S.H. and Soltanian-Zadeh, H. (2011). Automatic recognition of five types of white blood cells in peripheral blood,
Computerized Medical Imaging and Graphics 35 (4): 333–343. - Sajjad, M., Khan, S., Jan, Z., Muhammad, K., Moon, H., Kwak, J.T., Rho, S., Baik, S.W. and Mehmood, I. (2017). Leukocytes classification and segmentation in microscopic blood smear: A resource-aware healthcare service in smart cities,
IEEE Access 5 : 3475–3489. - Sakaguchi, K., Akimoto, K., Takaira, M., Tanaka, R.-i., Shimizu, T. and Umezu, S. (2022). Cell-based microfluidic device utilizing cell sheet technology,
Cyborg and Bionic Systems 2022 : 9758187. - Sarrafzadeh, O., Rabbani, H., Talebi, A. and Banaem, H.U. (2014). Selection of the best features for leukocytes classification in blood smear microscopic images,
Medical Imaging 2014: Digital Pathology, San Diego, USA , pp. 159–166. - Shahzad, A., Raza, M., Shah, J.H., Sharif, M. and Nayak, R.S. (2022). Categorizing white blood cells by utilizing deep features of proposed 4B-additionNet-based CNN network with ant colony optimization,
Complex & Intelligent Systems 8 : 3143–3159. - Shapiro, S.S. and Wilk, M.B. (1965). An analysis of variance test for normality (complete samples),
Biometrika 52 (3–4): 591–611. - Sharma, S., Gupta, S., Gupta, D., Juneja, S., Gupta, P., Dhiman, G. and Kautish, S. (2022). Deep learning model for the automatic classification of white blood cells,
Computational Intelligence and Neuroscience 2022 : 7384131. - Su, M.-C., Cheng, C.-Y. and Wang, P.-C. (2014). A neural-network-based approach to white blood cell classification,
The Scientific World Journal 2014 : 796371. - Sun, T., Lv, J., Zhao, X., Li, W., Zhang, Z. and Nie, L. (2023). In vivo liver function reserve assessments in alcoholic liver disease by scalable photoacoustic imaging,
Photoacoustics 34 : 100569. - Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. and Rabinovich, A. (2015). Going deeper with convolutions,
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, USA , pp. 1–9. - Tan, M. and Le, Q. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks,
Proceedings of the 36th International Conference on Machine Learning, Long Beach, USA , pp. 6105–6114. - Tavakoli, S., Ghaffari, A., Kouzehkanan, Z.M. and Hosseini, R. (2021). New segmentation and feature extraction algorithm for classification of white blood cells in peripheral smear images,
Scientific Reports 11 (1): 19428. - Turner, J.R. and Thayer, J. (2001).
Introduction to Analysis of Variance: Design, Analysis & Interpretation: Design, Analyis & Interpretation , Sage, Thousand Oakes. - Weatherspoon, D. (2024). What to know about white blood cells,
Medical News Today ,https://www.medicalnewstoday.com/articles/327446 . - Yao, X., Sun, K., Bu, X., Zhao, C. and Jin, Y. (2021). Classification of white blood cells using weighted optimized deformable convolutional neural networks,
Artificial Cells, Nanomedicine, and Biotechnology 49 (1): 147–155. - Yu, Y., Wan, M., Qian, J., Miao, D., Zhang, Z. and Zhao, P. (2024). Feature selection for multi-label learning based on variable-degree multi-granulation decision-theoretic rough sets,
International Journal of Approximate Reasoning 169 : 109181. - Zhang, C., Ge, H., Zhang, S., Liu, D., Jiang, Z., Lan, C., Li, L., Feng, H. and Hu, R. (2021). Hematoma evacuation via image-guided para-corticospinal tract approach in patients with spontaneous intracerebral hemorrhage,
Neurology and Therapy 10 : 1001–1013. - Zhang, R., Tan, J., Cao, Z., Xu, L., Liu, Y., Si, L. and Sun, F. (2024). Part-aware correlation networks for few-shot learning,
IEEE Transactions on Multimedia 26 : 9527–9538. - Zhu, C. (2024). Computational intelligence-based classification system for the diagnosis of memory impairment in psychoactive substance users,
Journal of Cloud Computing 13 (1): 119.
Language: English
Page range: 579 - 595
Submitted on: May 7, 2024
Accepted on: Aug 30, 2024
Published on: Dec 25, 2024
Published by: Sciendo
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
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© 2024 Muhammad Awais, Tallha Akram, Areej Alasiry, Mehrez Marzougui, Jongwoon Park, Byoungchol Chang, published by Sciendo
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