Enhanced COVID-19 Detection Through Combined Image Enhancement and Deep Learning Techniques

Abderrazak Benchabane, Fella Charif

Abstract


The rapid spread of COVID-19 has highlighted the need for automated patient data analysis to enable faster and more accurate diagnosis. Using pre-trained deep learning models on X-ray images has shown potential for effective COVID-19 detection. However, the performance of these models is highly dependent on the quality and quantity of training data. To address these challenges, enhancing the visual quality of X-ray images is critical for reliable virus detection. This study evaluates and combines three image enhancement techniques—Histogram Equalization, Contrast-Limited Adaptive Histogram Equalization (CLAHE), and Gamma Correction—to determine the optimal approach for improving detection accuracy. A dataset comprising 125 chest X-ray images from COVID-19-positive patients and 500 images from non-COVID-19 cases was used. The images were preprocessed using the enhancement techniques, and the enhanced datasets were employed to train ResNet50 and DenseNet201 models. Simulation results demonstrate that enhanced images consistently yield higher detection accuracy than unenhanced images. Among the techniques tested, combining Histogram Equalization, CLAHE, and Gamma Correction with the DenseNet201 model achieved the highest performance, attaining a remarkable accuracy of 99.03%. This outperforms previous methods, including the DarkCovidNet model, which achieved an accuracy of 98.08% on the same dataset.


Full Text:

PDF

References


Janko V, Slapničar G, Dovgan E, Reščič N, Kolenik T, Gjoreski M, Smerkol M, Gams M, Luštrek M. Machine Learning for Analyzing Non-Countermeasure Factors Affecting Early Spread of COVID-19. International Journal of Environment Research and Public Health, 18(13):6750, 2021.

https://doi.org/10.3390/ijerph18136750

Rajkumar S, Rajaraman PV, Meganathan HS, Sapthagirivasan V, ejaswinee V, Ashwin R. COVID-detect: a Deep Learning Approach for Classification of Covid-19 Pneumonia From Lung Segmented Chest Xrays. Biomedical Engineering: Applications, Basis and Communications 33(2), 2021.

https://doi.org/10.4015/S1016237221500101

Gams M, Kolenik, T. Relations between Electronics, Artificial Intelligence and Information Society through Information Society Rules. Electronics, 10(4), 514, 2021.

https://doi.org/10.3390/electronics10040514

Tahir A, Qiblawey Y, Khandakar A, Rahman T, Khurshid U, Musharavati F, Islam MT, Kiranyaz S, Al-Maadeed S, Chowdhury MEH. Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-ray Images. Cognitive Computation. 14:1752-1772, 2022.

https://doi.org/10.1007/s12559-021-09955-1

Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR. Automated detection of covid-19 cases using deep neural networks with x-ray images, Computers in Biology and Medicine. 121(103792), 2020.

https://doi.org/10.1016/j.compbiomed.2020.103792

Purohit K, Kesarwani A, Ranjan Kisku D, Dalui M. COVID-19 Detection on Chest X-Ray and CT Scan Images Using Multi-image Augmented Deep Learning Model. Advances in Intelligent Systems and Computing, 1412:395–413, 2022.

http://dx.doi.org/10.1007/978-981-16-6890-6_30

Narin A, Kaya C, Pamuk Z. Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks, Pattern Analysis and Applications 24(3):1207–1220, 2020.

https://doi.org/10.1007/s10044-021-00984-y

Sarki R, Ahmed K, Wang H, Zhang Y, Wang K. Automated detection of COVID-19 through convolutional neural network using chest x-ray images. PLoS ONE 17(1), 2022.

https://doi.org/10.1371/journal.pone.0262052

Masud, M. A light-weight convolutional Neural Network Architecture for classification of COVID-19 chest X-Ray images. Multimedia Systems, 28:1165–1174, 2022.

https://doi.org/10.1007/s00530-021-00857-8

Ravi, V, Narasimhan, H, Chakraborty, C et al. Deep learning-based metaclassifier approach for COVID-19 classification using CT scan and chest X-ray images. Multimedia Systems, 28:1401–1415, 2022.

https://doi.org/10.1007/s00530-021-00826-1

Asif S, Zhao M, Tang F et al. A deep learning-based framework for detecting COVID-19 patients using chest X-rays. Multimedia Systems, 28:1495–1513, 2022.

https://doi.org/10.1007/s00530-022-00917-7

Tahir A, Qiblawey Y, Khandakar A, Rahman T, Khurshid U, Musharavati F, Islam MT, Kiranyaz S, Al-Maadeed S, Chowdhury MEH. Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images. Computers in Biology and Medicine,132, 2021.

https://doi.org/10.1016/j.compbiomed.2021.104319

Kandhway P, Bhandari AK, Singh A. A novel reformed histogram equalization based medical image contrast enhancement using krill herd optimization, Biomedical Signal Processing and Control, 56 (101677), 2020.

https://doi.org/10.1016/j.bspc.2019.101677

Zhang J, Xie Y, Pang G, Liao Z, Verjans J, Li W, Sun Z, He J, Li Y, Shen C et al. Viral Pneumonia Screening on Chest X-Rays Using Confidence Aware Anomaly Detection. IEEE Transaction on Medical Imaging, 40(3): 879–890, 2021.

https://doi.org/10.1109/tmi.2020.3040950

Deng X, Shao H, Shi L, Wang X, Xie T. A classification–detection approach of COVID-19 based on chest X-ray and CT by using keras pretrained deep learning models. Computer Modeling in Engineering & Sciences. 125(2):579–596, 2020.

https://doi.org/10.32604/cmes.2020.011920

Wang Z, Zhang K, Wang B. Detection of COVID-19 Cases Based on Deep Learning with X-ray Images. Electronics. 11(21):3511, 2022.

https://doi.org/10.3390/electronics11213511

Apostolopoulos, I.D, Mpesiana T.A. COVID-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Physical and Engineering Sciences in Medicine. 43: 635–640, 2020.

https://doi.org/10.1007/s13246-020-00865-4

Mahmud T, Rahman A, Fattah S.A. CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multireceptive feature optimization. Computers in Biology and Medicine, 122, 103869,2020.

https://doi.org/10.1016/j.compbiomed.2020.103869

Mohit K, Dhairyata S, Vinod K, and Wanich S. COVID-19 prediction through X-ray images using transfer learning-based hybrid deep learning approach. MaterialsToday: Proceeding. 51: 2520–2524, 2022.

https://doi.org/10.1016/j.matpr.2021.12.123

Guefrechi S, Jabra M. B, Ammar A, Koubaa A, and Hamam H. Deep learning-based detection of COVID-19 from chest X-ray images. Multimedia tools and applications, 80: 31803-31820, 2021.

https://doi.org/10.1007/s11042-021-11192-5

Feki I, Ammar S, Kessentini Y, Muhammad K. Federated learning for COVID-19 screening from Chest X-ray images, Applied Soft Computing, 106, 2021.

https://doi.org/10.1016/j.asoc.2021.107330.

Mohan A, Ftsum bAa, Beshir K, Takore TT. A Hybrid Deep Learning CNN model for COVID-19 detection from chest X-rays, Heliyon, 10(5), 2024.

https://doi.org/10.1016/j.heliyon.2024.e26938.

Malik H, Naeem A, Naqvi, R A, and Loh, W. K. DMFL-Net: A Federated Learning-Based Framework for the Classification of COVID-19 from Multiple Chest Diseases Using Xrays. Sensors, 23(2):743, 2023.

https://doi.org/10.3390/s23020743

Gulmez, B. A novel deep neural network model based Xception and genetic algorithm for detection of COVID-19 from Xray images. Annals of Operations Research, 328:617–641, 2022.

https://doi.org/10.1007/s10479-022-05151-y

Zakariya A, Oraibi SA. Efficient COVID-19 Prediction by Merging Various Deep Learning Architectures, Informatica 48(5): 55–62, 2024.

https://doi.org/10.31449/inf.v48i5.5424

He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2016.

https://doi.org/10.1109/CVPR.2016.90

Huang G, Liu Z, Van Der Maaten L, Weinberger KQ. Densely connected convolutional networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2017.

https://doi.ieeecomputersociety.org/10.1109/CVPR.2017.243

Patel S, Patel L. Deep Learning Architectures and its Applications: A Survey, International journal of computer sciences and engineering. 6(6):1177-1183, 2018 .

http://dx.doi.org/10.26438/ijcse/v6i6.11771183

Zakariya A. Oraibi, Safaa Albasri. A Robust End-to-End CNN Architecture for Efficient COVID-19 Prediction form X-ray Images with Imbalanced Data, Informatica, 47(7):115–126, 2023.

https://doi.org/10.31449/inf.v47i7.4790




DOI: https://doi.org/10.31449/inf.v49i16.5869

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.