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Abstract
In recent years, convolutional neural networks (CNNs) have achieved amazing success in a variety of image categorization tasks. However, the architecture of CNNs has a significant impact on their performance. The designs of the most cutting-edge CNNs are frequently hand-crafted by experts in both CNNs and the topics under investigation. As a result, it's tough for users who don't have a lot of experience with CNNs to come up with the best CNN architecture for their individual image categorization challenges. This work investigates the application of the Fibonacci numbers to efficiently solve picture classification challenges by utilizing the hyperparameter of image dimension of COVID and non-COVID x-ray images. The suggested algorithm's greatest strength is the development of a CNN model that can be utilized for COVID viral prognosis using x-ray images to supplement existing COVID pandemic testing techniques. The proposed approach is tested using the metrics of training time, accuracy, precision, recall, and F1-score on commonly used benchmark image classification datasets. According to the experimental data, the CNN model with an image dimension of 55 x 55 surpasses the other CNN models in terms of training time, accuracy, recall, and F1-score. Several issues were raised about how to choose the best CNN models for prognostic picture categorization.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
Torralba, E. M. (2022). Fibonacci Numbers as Hyperparameters for Image Dimension of a Convolu-tional Neural Network Image Prognosis Classification Model of COVID X-ray Images. International Journal of Multidisciplinary: Applied Business and Education Research, 3(9), 1703-1716. https://doi.org/10.11594/ijmaber.03.09.11
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den Eynde, J., Lachmann, M., Laugwitz, K.-L., Manlhiot, C., & Kutty, S. (2022). Successfully implemented artificial intelligence and machine learning applications in cardiology: State-of-the-art review. Trends in Cardiovascular Medicine, https://doi.org/10.1016/j.tcm.2022.01.010.
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Han, D., Liu, Q., & Fan, W. (2018). A new image classification method using CNN transfer learning and web data augmentation. Expert Systems with Applications, 95, 43-56. https://doi.org/10.1016/j.eswa.2017.11.028.
Hashmi, H., & Asif, H. (2020). Early detection and assessment of covid-19. Frontiers in medicine, 7, 311. https://doi.org/10.3389/fmed.2020.00311.
Islam, M., Hasan, M., Wang, X., Germack, H. D., & Noor-E-Alam, M. (2018). A systematic review on healthcare analytics: application and theoretical perspective of data mining. In Healthcare, 6(2), 54. https://dx.doi.org/10.3390%2Fhealthcare6020054.
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James, L. J., Wong, G., Tong, A., Craig, J. C., Howard, K., & Howell, M. (2021). Patient preferences for cancer screening in chronic kidney disease: a best–worst scaling survey. Nephrology Dialysis Transplantation, https://doi.org/10.1093/ndt/gfab360.
Jimoh, R. G., Abisoye, O. A., & Uthman, M. B. (2022). Ensemble feed-forward neural network and support vector machine for prediction of multiclass malaria infection. Journal of Information and Communication Technology, 21(1), 117-148. https://doi.org/10.32890/jict2022.21.1.6.
Johnson, K. W., Torres Soto, J., Glicksberg, B. S., Shameer, K., Miotto, R., Ali, M., . . . Dudley, J. T. (2018). Artificial Intelligence in Cardiology. Journal of the American College of Cardiology, 71(23), 2668-2679. https://doi.org/10.1016/j.jacc.2018.03.521.
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Kader, N., Clement, N. D., Patel, V. R., Caplan, N., Banaszkiewicz, P., & Kader, D. (2020). The theoretical mortality risk of an asymptomatic patient with a negative SARS-CoV-2 test developing COVID-19 following elective orthopaedic surgery. The Bone and Joint Journal, 1256-1260. https://doi.org/10.1302/0301-620X.102B9.BJJ-2020-1147.R1.
Kang, J., Jang, Y., Kim, J., Han, S.-H., Lee, K., Kim, M., & Eom, J. (2020). South Korea's responses to stop the COVID-19 pandemic. American Journal of Infection Control, 48(9), 1080-1086. https://doi.org/10.1016/j.ajic.2020.06.003.
Kaye, L., Theye, B., Smeenk, I., Gondalia, R., Barrett, M. A., & Stempel, D. A. (2020). Changes in medication adherence among patients with asthma and COPD during the COVID-19 pandemic. The journal of allergy and clinical immulogy. In practice, 8(7), 2384-2385. https://dx.doi.org/10.1016%2Fj.jaip.2020.04.053.
Khan, A., Khan, S., Harouni, M., Abbasi, R., Iqbal, S., & Mehmood, Z. (2021). Brain tumor segmentation using K-means clustering and deep learning with synthetic data augmentation for classification. Microsc Res Tech, 84(7), 1389-1399. https://doi.org/10.1002/jemt.23694.
Kingma, D. P., & Ba, J. (2017, January). Adam: A Method for Stochastic Optimization. Retrieved April 26, 2022 from arXiv: https://arxiv.org/abs/1412.6980
Kumar, N., & Kumar, D. (2021). An improved grey wolf optimization-based learning of artificial neural network for medical data classification. Journal of Information and Communication Technology, 20(2), 213-248. https://doi.org/10.32890/jict2021.20.2.4.
Lalwani, P., Mishra, M., Chadha, a., & Sethi, P. (2022). Customer churn prediction system: a machine learning approach. Computing, 104, 271-294. https://doi.org/10.1007/s00607-021-00908-y.
LeCun, Y. (1988). A theoretical framework for back-propagation. In Proceedings of the 1988 connectionist models summer school (pp. 21-28).
Li, J.-Y., Zhan, Z.-H., Xu, J., Kwong, S., & Zhang, J. (2021). Surrogate-assisted hybrid-model estimation of distribution algorithm for mixed-variable hyperparameters optimization in convolutional neural networks. IEEE Transactions on Neural Networks and Learning Systems, https://doi.org/10.1109/tnnls.2021.3106399.
Lin, G., & Shen, W. (2018). Research on convolutional neural network based on improved Relu piecewise activation function. Procedia Computer Science, 131, 977-984. https://doi.org/10.1016/j.procs.2018.04.239.
Long, L., & Corsar, K. (2020). The COVID-19 effect: number of patients presenting to The Mid Yorkshire Hospitals OMFS team with dental infections before and during The COVID-19 outbreak. Br J Oral Maxillofac Surg, 58(6), 713-714. Br J Oral Maxillofac Surg.
Maissenhaelter, B. E., Woolmore, A. L., & Schlag, P. M. (2018). Real-world evidence research based on big data. Onkologe, 24, 91-98. https://doi.org/10.1007/s00761-018-0358-3.
Mellor, S. (2022, January 10). Covid inequality worsened by antigen test supply crunch. Retrieved April 1, 2022 from Fortune: https://fortune.com/2022/01/10/omicron-testing-shortages-delays-covid-inequality/
Menon, V., & Padhy, S. (2020). Ethical dilemmas faced by health care workers during COVID-19 pandemic: Issues, implications and suggestions. Asian journal of psychiatry, 51, https://dx.doi.org/10.1016%2Fj.ajp.2020.102116.
More, S. S., Mange, M., Sankhe, M., & Sahu, S. (2021). Convolutional Neural Network based Brain Tumor Detection. In 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1532-1538. doi: 10.1109/ICICCS51141.2021.9432164).
Nayak, D., Das, D., Dash, R., Majhi, S., & Majhi, B. (2020). Deep extreme learning machine with leaky rectified linear unit for multiclass classification of pathological brain images. Multimedia Tools and Applications, 79, 15381–15396. https://doi.org/10.1007/s11042-019-7233-0.
Pedersen, E. (2020). The Beauty of Mathematical Order: a Study of the Role of Mathematics in Greek Philosophy and Modern Art Works of Piet Hein and Inger Christensen. The Journal of Somaesthetics, 6, https://doi.org/10.5278/ojs.jos.v6i1.3667.
Rajagopal, A., Joshi, G., Ramachandran, A., Subhalakshmi, R. T., Khari, M., Jha, S., . . . You, J. (2020). A deep learning model based on multi-objective particle swarm optimization for scene classification in unmanned aerial vehicles. IEEE Access, 8, 135383-135393. https://doi.org/10.1109/ACCESS.2020.3011502.
Scheps, R. (2020). Geometry and the Life of Forms. In Mathematics in the Visual Arts (pp. 29-52. https://doi.org/10.1002/9781119801801.ch2).
Schmitt, F., Banu, R., Yeom, I.-T., & Do, K. (2018). Development of artificial neural networks to predict membrane fouling in an anoxic-aerobic membrane bioreactor treating domestic wastewater. Biochemical Engineering Journal, 133, 47-58. https://doi.org/10.1016/j.bej.2018.02.001.
Seki, T., Takeuchi, M., & Kawakami, K. (2021). Eating and drinking habits and its association with obesity in Japanese healthy adults: retrospective longitudinal big data analysis using a health check-up database. British Journal of Nutrition, 126(10), 1585-1591. https://doi.org/10.1017/s0007114521000179.
Shafique, M., Khurshid, M., Rahman, H., Khanna, A., & Gupta, D. (2019). The role of big data predictive analytics and radio frequency identification in the pharmaceutical industry. IEEE Access, 7, 9013-9021. https://doi.org/10.1109/ACCESS.2018.2890551.
Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., . . . Shen, D. (2020). Review of artificial intelligence techniques in imaging data acquisition, segmentation, and diagnosis for COVID-19. IEEE reviews in biomedical engineering, 14, 4-15. https://doi.org/10.1109/RBME.2020.2987975.
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