Authors : James Olujoba Adegboye; Wasiu Oladimeji Ismaila; Adeleye Samuel Falohun; Folasade Muibat Ismaila; Abiodun Adebayo Owolabi

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/5azvtjp7

Scribd : https://tinyurl.com/3y7t4c4r

DOI : https://doi.org/10.38124/ijisrt/25nov464

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Convolutional Neural Network (CNN) is a machine learning method which mainly focused on the automatic feature selection and matching of images and has been used for detection and recognition. CNN suffers from hyperparameter selection and overfitting problem and can be solved using an optimization technique. Existing optimization technique such as Mayfly Algorithm (MA) still suffers from initial parameter tuning and had slow convergence behaviour. This research developed a Mayfly Algorithm based on Convolutional Neural Network for pulmonary diseases recognition. The X-ray images which include normal and pulmonary diseases cases were obtained from a repository via www.kaggle.com. The images were pre-processed using cropping, contrast adjustment, histogram equalizer and normalization to obtain good images quality. A Mayfly Algorithm was used to optimize CNN hyperparameters. The developed technique was implemented in MATLAB (R2020a) Software. The results obtained were evaluated using standard metric. The CNN technique average results are 96.0%, 94.6%, 3.7%, 95.4% and 82.4μs while MA-CNN average results are 97.1%, 95.9, 3.0%, 96.7% and 60.7μs for Specificity, sensitivity, false positive rate, Accuracy and Computation time respectively at 0.75 threshold. This shows the effectiveness of optimizing CNN hyperparameters for image recognition.

Keywords : Mayfly Algorithm, Convolutional Neural Network, Pulmonary Diseases, Hyperparameters, Optimization Technique.

References :

1. Md M. A., Shahana A. L., Zahed S. (2022). Machine-Learning-Based Disease Diagnosis: A Comprehensive Review, Healthcare (Basel), 10(3):541.
2. Zhu, N., Zhang, D., Wang, W., Li, X., Yang, B., Song, J., (2020). A novel coronavirus from patients with pneumonia in China, 2019. N Engl J Med. DOI: 10.1056/NEJMoa2001017.
3. Lai, C.C., Liu, Y.H., Wang, C.Y., Wang, Y.H., Hsueh, S.C., Yen, M.Y., Ko, W.C. and Hsueh, P.R. (2020). Asymptomatic carrier state, acute respiratory disease, and pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2): Facts and myths. Journal of Microbiology, Immunology and Infection, 53(3), 404-412.
4. Lal Hussain, Tony Nguyen, Haifang Li, Adeel A. Abbasi, Kashif J. Lone, Zirun Zhao, Mahnoor Zaib, Anne Chen and Tim Q. Duong (2020) Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection. BioMed Eng OnLine 19, 88 (2020). https://doi.org/10.1186/s12938-020-00831-x
5. Prem, K., Liu, Y., Russell, T.W., Kucharski, A.J., Eggo, R.M., Davies, N., Flasche, S., Clifford, S., Pearson, C.A., Munday, J.D. and Abbott, S., (2020). The effect of control strategies to reduce social mixing on outcomes of the COVID-19 epidemic in Wuhan, China: a modelling study. The Lancet Public Health, 5(5), e261-e270.
6. Kumar Rahul, Ridhi Arora, Vipul Bansal, Vinodh J Sahayasheela, Himanshu Buckchash, Javed Imran, Narayanan Narayanan, Ganesh N Pandian,  and Balasubramanian Raman (2020) Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers.
7. Kumar, R., Arora, A., Bansal, B., Sahayasheela, V.J., Buckchash, H., Imran, J., Narayanan, N., Pandian, G.N. and Raman, B (2020). Accurate prediction of COVID-19 using chest x-ray images through deep feature learning model with smote and machine learning classifiers. MedRxiv. 1-10.
8. Mohaimenul A. R., Sadman S., Nur M. F., Abdullah A., Md. A. R., Swakkhar S., Md. S. H. Muktac (2024). A systematic review of hyperparameter optimization techniques in Convolutional Neural Networks Decision Analytics Journal, 11(2024) 100470.
9. Wang, D., Mo, J., Zhou, G., Xu, L., and Liu, Y. (2020). An efficient mixture of deep and machine learning models for COVID-19 diagnosis in chest X-ray images. PloS one, 15(11), 10-18.
10. Goel, T., Murugan, R., Mirjalili, S., and Chakrabartty, D. K. (2021). OptCoNet: an optimized Convolutional neural network for an automatic diagnosis of COVID-19. Applied Intelligence, 51(3), 1351-1366.
11. Castiglioni, I., Ippolito, D., Interlenghi, M., Monti, C. B., Salvatore, C., Schiaffino, S. and Sardanelli, F. (2021). Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy. European Radiology Experimental, 5(1), 1-10.
12. Abbas, A., Abdelsamea, M. M. and Gaber, M. M. (2021). Classification of COVID-19 in chest X-ray images using De TraC deep Convolutional neural network. Applied Intelligence, 51(2), 854-864.
13. J.  O. Adegboye, W. O. Ismaila, Adeleye Samuel Falohun, Abiodun Adebayo Owolabi, Folasade Muibat Ismaila (2025) Performance Evaluation of LBP, Mutual Information and CNN in Digital Diseases  Image Detection. International Journal of Innovative Science and Research Technology,   10(8), 2193-2202. https://doi.org/10.38124/ijisrt/25aug140
14. Houssein, E. H., Abohashima, Z., Elhoseny, M., and Mohamed, W. M. (2022). Hybrid quantum-classical Convolutionalal neural network model for COVID-19 prediction using chest X-ray images. Journal of Computational Design and Engineering, 9(2), 343-363.
15. Gayathri, J. L., Abraham, B., Sujarani, M. S., and Nair, M. S. (2022). A computer-aided diagnosis system for the classification of COVID-19 and non-COVID-19 pneumonia on chest X-ray images by integrating CNN with sparse autoencoder and feed forward neural network. Computers in biology and medicine, 141, 105134.
16. Bhosale, Y. H., and Patnaik, K. S. (2023). PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep Convolutional neural network from chest X-ray images to minimize severity and mortality rates. Biomedical Signal Processing and Control, 81, 104445.
17. An, Q., Chen, W., Shao, W.A (2024) Deep Convolutional Neural Network for Pneumonia     Detection in X-ray Images with Attention Ensemble. Diagnostics 2024, 14,390. https://doi.org/10.3390/diagnostics14040390
18. Ismaila W. O..,  Ismaila Folasade. M, Falohun Adeleye S: Soft Computing Approach To Multi-Modal Biometric System, American International Journal of  Research In Science, Technology, Engineering & Mathematics, 20(1), pp. 66-72. 2017.
19. Ismaila W. O., Adetunji A., Falohun A. and Iwashokun G. B. (2012). A Study of Features Extraction Algorithms for Human Face Recognition. Transnational Journal of Science and Technology,  2(6), 14-22.
20. Biju, R., Patel, W., Suresh Manic, K. and Rajinikanth, V. (2022). Framework for classification of chest X-rays into normal/COVID-19 using Brownian-Mayfly-Algorithm selected hybrid features. Mathematical Problems in Engineering.1-9.
21. Zervoudakis K, and Tsafarakis S. (2020). A Mayfly optimization algorithm, Computers and Industrial Engineering; (2020).
22. Tajbakhsh, N., Shin, J. Y., Gurudu, S. R., Hurst, R. T., Kendall, C. B., Gotway, M. B., and Liang, J. (2016). Convolutional neural networks for medical image analysis: Full training or fine tuning. IEEE transactions on medical imaging, 35(5), 1299-1312.
23. Umer, M., Ashraf, I., Ullah, S., Mehmood, A., and Choi, G. S. (2022). COVINet: a Convolutionalal neural network approach for predicting COVID-19 from chest X-ray images. Journal of Ambient Intelligence and Humanized Computing, 1-13.
24. Sinha, T., Verma, B., and Haidar, A. (2017). Optimization of Convolutional neural network parameters for image classification. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI) (1-7). IEEE.
25. Ismaila Folasade M., Afolabi Adeolu O, Alo Oluwaseun O, Ismaila W. Oladimeji, Funmilayo A.Ajala (2024). Comparative Analysis of Selected Evolutionary Algorithms as Feature Selectors in Digital Face Image Processing. International Journal of Recent Research in Mathematics, Computer Science and Information Technology, 10(2), 53-62.
26. Narin, A., Kaya, C., and Pamuk, Z. (2021). Automatic detection of coronavirus disease (covid-19) using x-ray images and deep Convolutional neural networks. Pattern Analysis and Applications, 1-14.