A Dual Framework of Feature Selection based on the Fusion of HOG and Pyramid HOG for the Categorization of COVID-19


  • Mehak Mushtaq Malika
  • Muhammand Hamza Azam
  • Farhat Afza


COVID, Classification, Entropy, HOG, PHOG, SVM


COVID-19 was initially detected in Wuhan, China. The virus spread all over the world at a rapid speed. COVID-19 is an infection that may cause infections in the respiratory system and the lungs. In order to diagnose COVID-19, chest X-rays have been utilized extensively. The purpose of this research is to create a computer-vision-based method for identifying COVID in chest X-rays. In the proposed model handcrafted features known as HOG and PHOG are derived and fused. After features have been fused, the Binary Grey Wolf Optimization technique is used with an Entropy-Based Optimization Algorithm to select the most significant features possible. The proposed model results are evaluated on a benchmark X-ray dataset that gives greater than 99% accuracy. The proposed model performed better compared to existing published works in this domain.


A. Tahamtan, and A. Ardebili, “Real-time RT-PCR in COVID-19 detection: issues affecting the results,” Expert review of molecular diagnostics, vol. 20, no. 5, pp. 453-454, 2020.

Y. Fan, J. Liu, R. Yao, and X. Yuan, “COVID-19 detection from X-ray images using multi-kernel-size spatial-channel attention network,” Pattern Recognition, vol. 119, pp. 108055, 2021.

A. M. Fayyaz, K. A. Al-Dhlan, S. U. Rehman, M. Raza, W. Mehmood, M. Shafiq, and J.-G. Choi, “Leaf blights detection and classification in large scale applications,” Intellegent automation and soft computing, vol. 31, no. 1, pp. 507-522, 2022.

A. Shelke, M. Inamdar, V. Shah, A. Tiwari, A. Hussain, T. Chafekar, and N. Mehendale, “Chest X-ray classification using deep learning for automated COVID-19 screening,” SN computer science, vol. 2, no. 4, pp. 1-9, 2021.

H. Farhat, G. E. Sakr, and R. Kilany, “Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19,” Machine vision and applications, vol. 31, no. 6, pp. 1-42, 2020.

A. Muiz Fayyaz, M. Kolivand, J. Alyami, S. Roy, and A. Rehman, "Computer Vision-Based Prognostic Modelling of COVID-19 from Medical Imaging," Prognostic Models in Healthcare: AI and Statistical Approaches, pp. 25-45: Springer, 2022.

S. O. Folorunso, J. B. Awotunde, N. O. Adeboye, and O. E. Matiluko, "Data Classification Model for COVID-19 Pandemic," Advances in Data Science and Intelligent Data Communication Technologies for COVID-19, pp. 93-118: Springer, 2022.

S. A. Mahmoudi, S. Stassin, M. E. H. Daho, X. Lessage, and S. Mahmoudi, "Explainable Deep Learning for Covid-19 Detection Using Chest X-ray and CT-Scan Images," Healthcare Informatics for Fighting COVID-19 and Future Epidemics, pp. 311-336: Springer, 2022.

G. Kumar, and P. K. Bhatia, "A detailed review of feature extraction in image processing systems." pp. 5-12.

D. ping Tian, “A review on image feature extraction and representation techniques,” International Journal of Multimedia and Ubiquitous Engineering, vol. 8, no. 4, pp. 385-396, 2013.

D. A. Ragab, and O. Attallah, “FUSI-CAD: Coronavirus (COVID-19) diagnosis based on the fusion of CNNs and handcrafted features,” PeerJ Computer Science, vol. 6, pp. e306, 2020.

R. Das, M. Arshad, P. Manjhi, and S. D. Thepade, "Covid-19 Identification with Chest X-Ray Images merging Handcrafted and Automated Features for Enhanced Feature Generalization." pp. 1-6.

L. Hussain, T. Nguyen, H. Li, A. A. Abbasi, K. J. Lone, Z. Zhao, M. Zaib, A. Chen, and T. Q. Duong, “Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection,” BioMedical Engineering OnLine, vol. 19, no. 1, pp. 1-18, 2020.

Ş. Öztürk, U. Özkaya, and M. Barstuğan, “Classification of Coronavirus (COVID‐19) from X‐ray and CT images using shrunken features,” International Journal of Imaging Systems and Technology, vol. 31, no. 1, pp. 5-15, 2021.

H. Kang, L. Xia, F. Yan, Z. Wan, F. Shi, H. Yuan, H. Jiang, D. Wu, H. Sui, and C. Zhang, “Diagnosis of coronavirus disease 2019 (covid-19) with structured latent multi-view representation learning,” IEEE transactions on medical imaging, vol. 39, no. 8, pp. 2606-2614, 2020.

M. A. Elaziz, K. M. Hosny, A. Salah, M. M. Darwish, S. Lu, and A. T. Sahlol, “New machine learning method for image-based diagnosis of COVID-19,” Plos one, vol. 15, no. 6, pp. e0235187, 2020.

J. Redmon, "Darknet: Open source neural networks in c," 2013.

N. F. P. Setyono, D. Chahyati, and M. I. Fanany, "Betawi Traditional Food Image Detection using ResNet and DenseNet." pp. 441-445.

S. Targ, D. Almeida, and K. Lyman, “Resnet in resnet: Generalizing residual architectures,” arXiv preprint arXiv:1603.08029, 2016.

M. Z. Islam, M. M. Islam, and A. Asraf, “A combined deep CNN-LSTM network for the detection of novel coronavirus (COVID-19) using X-ray images,” Informatics in Medicine Unlocked, vol. 20, pp. 100412, 2020, 2020.

C. M. Vastrad, “Important Molecular Descriptors Selection Using Self Tuned Reweighted Sampling Method for Prediction of Antituberculosis Activity,” arXiv preprint arXiv:1402.5360, 2014.

D. Kermany, K. Zhang, and M. Goldbaum, “Labeled optical coherence tomography (oct) and chest x-ray images for classification,” Mendeley data, vol. 2, no. 2, 2018.

J. P. Cohen, P. Morrison, L. Dao, K. Roth, T. Q. Duong, and M. Ghassemi, “Covid-19 image data collection: Prospective predictions are the future,” arXiv preprint arXiv:2006.11988, 2020.

M. Gour, and S. Jain, “Automated COVID-19 detection from X-ray and CT images with stacked ensemble convolutional neural network,” Biocybernetics and Biomedical Engineering, vol. 42, no. 1, pp. 27-41, 2022.

R. A. Al-Falluji, Z. D. Katheeth, and B. Alathari, “Automatic detection of COVID-19 using chest X-ray images and modified ResNet18-based convolution neural networks,” Computers, Materials, & Continua, pp. 1301-1313, 2021.




How to Cite

Mehak Mushtaq Malika, Muhammand Hamza Azam, & Farhat Afza. (2023). A Dual Framework of Feature Selection based on the Fusion of HOG and Pyramid HOG for the Categorization of COVID-19. University of Wah Journal of Computer Science, 4(1), 1–7. Retrieved from http://uwjcs.org.pk/index.php/ojs/article/view/46