Enhancing COVID-19 Detection in X-Ray Images Through Deep Learning Models with Different Image Preprocessing Techniques
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| Publicat a: | International Journal of Advanced Computer Science and Applications vol. 16, no. 1 (2025) |
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| Autor principal: | |
| Publicat: |
Science and Information (SAI) Organization Limited
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| Matèries: | |
| Accés en línia: | Citation/Abstract Full Text - PDF |
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|---|---|---|---|
| 001 | 3168740314 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2158-107X | ||
| 022 | |a 2156-5570 | ||
| 024 | 7 | |a 10.14569/IJACSA.2025.0160161 |2 doi | |
| 035 | |a 3168740314 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 100 | 1 | |a PDF | |
| 245 | 1 | |a Enhancing COVID-19 Detection in X-Ray Images Through Deep Learning Models with Different Image Preprocessing Techniques | |
| 260 | |b Science and Information (SAI) Organization Limited |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The identification of COVID-19 using chest X-ray (CXR) images plays a critical role in managing the pandemic by providing a rapid, non-invasive, and accessible diagnostic tool. This study evaluates the impact of different image preprocessing techniques on the performance of deep learning models for COVID-19 classification based on COVID-19 Radiography Database, which includes 10,192 normal CXR images, 6012 lung opacity (non-COVID lung infection) images, and 1345 viral pneumonia images. Along with the images, corresponding lung masks are also included to aid in the segmentation and analysis of lung regions. Specifically, three convolutional neural network (CNN) models were developed, each using a distinct preprocessing method: Contrast Limited Adaptive Histogram Equalization (CLAHE), traditional histogram equalization, and no preprocessing. The results revealed that while the CLAHE-enhanced model achieved the highest training accuracy (93.26%) and demonstrated superior stability during training, it showed lower performance in the validation phase, with validation accuracy of 91.31%. In contrast, the model with no preprocessing, which exhibited slightly lower training accuracy (92.98%), outperformed the CLAHE model during validation, achieving the highest validation accuracy of 91.50% and the lowest validation loss. The histogram equalization model demonstrated performance similar to that of CLAHE but with slightly higher validation loss and accuracy compared to the unprocessed model. These findings suggest that while CLAHE excels in enhancing image details during training, it may lead to overfitting and reduced generalization ability. In contrast, the model without preprocessing showed the best generalization and stability, indicating that preprocessing techniques should be chosen carefully to balance feature enhancement with the need for generalization in real-world applications. This study underscores the importance of selecting appropriate image preprocessing techniques to enhance deep learning models' performance in medical image classification, particularly for COVID-19 detection. Histogram Equalization The results contribute to ongoing efforts to optimize diagnostic tools using AI and image processing. | |
| 653 | |a Accuracy | ||
| 653 | |a Preprocessing | ||
| 653 | |a Equalization | ||
| 653 | |a Performance evaluation | ||
| 653 | |a Lungs | ||
| 653 | |a Image segmentation | ||
| 653 | |a Histograms | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Medical imaging | ||
| 653 | |a Image classification | ||
| 653 | |a Stability | ||
| 653 | |a Deep learning | ||
| 653 | |a Machine learning | ||
| 653 | |a Image processing | ||
| 653 | |a Diagnostic software | ||
| 653 | |a Infections | ||
| 653 | |a Tomography | ||
| 653 | |a COVID-19 vaccines | ||
| 653 | |a Datasets | ||
| 653 | |a Computer science | ||
| 653 | |a Immunization | ||
| 653 | |a Patients | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Pandemics | ||
| 653 | |a Neural networks | ||
| 653 | |a X-rays | ||
| 773 | 0 | |t International Journal of Advanced Computer Science and Applications |g vol. 16, no. 1 (2025) | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3168740314/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3168740314/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |