Proceedings of International Conference on Applied Innovation in IT
2025/12/22, Volume 13, Issue 5, pp.605-611

Lung Disease Detection by Processing X-ray Image Database Using Deep Learning Techniques


Ahmed Khudhair Abbas, Ihsan Salman Jasim and Adil Ibrahim Khalil


Abstract: Integrating technology such as artificial intelligence in the medical field may contribute significantly to the early diagnosis of diseases. This study is conducted to achieve two goals: The first is to use three advanced deep learning models to identify tuberculosis and COVID-19 diseases from healthy cases by processing a chest X-ray image dataset. The second goal is to compare the three models and identify the most generalizable one based on accuracy and computational efficiency. The proposed models were applied to a large-scale chest X-ray of 6375 images. The findings show that InceptionV3 outperforms the CNNs and ResNet-50 algorithms by achieving high metrics values (Accuracy = 0.9636, Precision = 0.9752, Recall = 0.9554, and F1-Score = 0.9651). Furthermore, the results show that training neural networks across a wide range of epochs improves the model predictions of new data. The findings demonstrate InceptionV3's capability of providing a robust and automated method for classifying chest X-ray images. Apart from accuracy assessment, this computational analysis depicted marked variations in efficiency amongst the three models. The CNN architecture achieved the shortest training time of ≈12.4 minutes and attained the fastest inference speed of ≈4.1 ms per image, whereas ResNet-50 and InceptionV3 required longer training and inference times. This starkly indicates that there is a clear trade-off between computational cost and predictive performance across the evaluated architectures.

Keywords: Chest X-Ray Images, Classifications, InceptionV3, Medical Imaging, Database.

DOI: Under indexing

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References:

  1. E. L. Irede et al., “Medical imaging: a critical review on X-ray imaging for the detection of infection,” Biomed. Mater. Devices, Jul. 2024, doi: 10.1007/s44174-024-00212-1.
  2. G. Battineni, G. G. Sagaro, N. Chinatalapudi, and F. Amenta, “Applications of machine learning predictive models in the chronic disease diagnosis,” J. Pers. Med., vol. 10, no. 2, 2020, doi: 10.3390/jpm10020021.
  3. X. Liu et al., “A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis,” Lancet Digit. Health, vol. 1, no. 6, pp. e271–e297, 2019, doi: 10.1016/S2589-7500(19)30123-2.
  4. A. Khalil, A. Humeau-Heurtier, P. Abraham, and G. Mahe, “Comparative study to analyze the effect of aging on microvascular blood flow by processing laser speckle contrast images when Lorentzian and Gaussian velocity profiles are assumed for moving scatterers,” in Proc. Int. Conf. Image Process. Theory Tools Appl. (IPTA), 2014, pp. 1–6, doi: 10.1109/IPTA.2014.7001985.
  5. S. R. Nayak, D. R. Nayak, U. Sinha, V. Arora, and R. B. Pachori, “Application of deep learning techniques for detection of COVID-19 cases using chest X-ray images: a comprehensive study,” Biomed. Signal Process. Control, vol. 64, 2021, doi: 10.1016/j.bspc.2020.102365.
  6. Y. Singh, N. Tripathi, S. Yadav, N. Gupta, A. U. Kumar, and J. V. N. Ramesh, “Transfer learning and chest X-ray-based image processing and modeling to detect COVID-19,” in Smart Technologies in Healthcare Management: Pioneering Trends and Applications, CRC Press, 2024, pp. 240–263, doi: 10.1201/9781003330523-16.
  7. M. M. R. Khan et al., “Automatic detection of COVID-19 disease in chest X-ray images using deep neural networks,” in IEEE Region 10 Humanitarian Technol. Conf. (R10-HTC), 2020, doi: 10.1109/R10-HTC49770.2020.9357034.
  8. M. J. Awan, M. H. Bilal, A. Yasin, H. Nobanee, N. S. Khan, and A. M. Zain, “Detection of COVID-19 in chest X-ray images: a big data enabled deep learning approach,” Int. J. Environ. Res. Public Health, vol. 18, no. 19, p. 10147, 2021, doi: 10.3390/ijerph181910147.
  9. M. Mujahid et al., “Pneumonia classification from X-ray images with Inception-V3 and convolutional neural network,” Diagnostics, vol. 12, no. 5, 2022, doi: 10.3390/diagnostics12051280.
  10. S. Tang, S. Yuan, and Y. Zhu, “Convolutional neural network in intelligent fault diagnosis toward rotatory machinery,” IEEE Access, vol. 8, pp. 86510–86519, 2020, doi: 10.1109/ACCESS.2020.2992692.
  11. A. Rajbongshi et al., “Recognition of mango leaf disease using convolutional neural network models: a transfer learning approach,” Indones. J. Electr. Eng. Comput. Sci., vol. 23, no. 3, pp. 1681–1688, 2021, doi: 10.11591/ijeecs.v23.i3.pp1681-1688.
  12. S. Ganjei and D. K. Gunleiksrud, “Deep learning approach for binary classification of microscopic black holes and sphalerons: optimization by employing custom loss function,” 2023. [Online]. Available: https://hvlopen.brage.unit.no/hvlopen-xmlui/bitstream/handle/11250/3082038/Prosjekthandbok.pdf
  13. X. Chen et al., “Recent advances and clinical applications of deep learning in medical image analysis,” Med. Image Anal., vol. 79, 2022, doi: 10.1016/j.media.2022.102444.
  14. B. Dong, X. Fu, and X. Kang, “SSGNet: semi-supervised multi-path grid network for diagnosing melanoma,” Pattern Anal. Appl., vol. 26, no. 1, pp. 357–366, Feb. 2023, doi: 10.1007/s10044-022-01100-4.
  15. H. I. Hussein, A. O. Mohammed, M. M. Hassan, and R. J. Mstafa, “Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images,” Expert Syst. Appl., vol. 223, p. 119900, Aug. 2023, doi: 10.1016/j.eswa.2023.119900.
  16. G. Vilone and L. Longo, “Explainable artificial intelligence: a systematic review,” 2020. [Online]. Available: http://arxiv.org/abs/2006.00093
  17. Ž. Vujović, “Classification model evaluation metrics,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 6, pp. 599–606, 2021, doi: 10.14569/IJACSA.2021.0120670.


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