Original Research · Open access
Lung Cancer Detection Using A Hybridized Contrast-Based Xception Model On Image Data: A Pilot Study
Abstract
Lung cancer has since become the second largest direct cause of death when compared to other forms of cancer. Lung cancer is easily diagnosed via medical x-rays, MRIs and CT-scans. The use of learning schemes in identification of disease has become a veritable tool in medical frontiers. But, a major challenge with image classification – is attributed to the disease severity, and model’s ability to address feature enhancements in model designs for image processing. The models must capture intrinsic features with image enhancements in regions of interest – amongst other feats. Our study uses a median filter (AMF) with contrast enhancement (CLAHE) to normalize images, use a variation-mode decomposition as preprocessor to sustain multimodal image learning upheld by the Xception model. VMD handles complex image data to resolve mode mixing in its AMF and CLAHE. This is further upheld via localized context and hierarchical image representation in our CNN-based Xception model to yield improved training efficiency, model stability and generalization even with complex spatiotemporal dependencies inherent the image dataset. Our result shows the proposed method performs well in lung cancer detection, with significantly enhanced time convergence and reliability to yield a 1.000 Accuracy, 0.994 Precision, 0.989 Recall and 0.989 F1-score. Thus, the proposed model provides an effective image recognition and detection solution for lung cancer.
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Agboi, J., Onoma, P., Ojugo, A. A., Ugbotu, E. V., Aghaunor, T. C., Odiakaose, C. C., Eboka, A. O., Binitie, A. P., Ezzeh, P. O., Ejeh, P. O., Geteloma, V. O., Idama, R. O., Orobor, A. I., Onochie, C. C., & Obruche, C. O. (2025). Lung Cancer Detection Using A Hybridized Contrast-Based Xception Model On Image Data: A Pilot Study. International Journal of Advanced Computing and Intelligent System, 4(1), online first. https://doi.org/10.66917/ijacis.a000021
Agboi, Joy, et al. “Lung Cancer Detection Using A Hybridized Contrast-Based Xception Model On Image Data: A Pilot Study.” International Journal of Advanced Computing and Intelligent System, vol. 4, no. 1, 2025, pp. online first. doi:10.66917/ijacis.a000021
Agboi, Joy, Paul Onoma, Arnold Adim Ojugo, Eferhire Valentine Ugbotu, Tabitha Chukwudi Aghaunor, Chris Chukwufunaya Odiakaose, Andrew Okonji Eboka, Amaka Patience Binitie, Peace Oguguo Ezzeh, Patrick Ogholuwarami Ejeh, Victor Ochuko Geteloma, Rebecca Okeoghene Idama, Anderson Ise Orobor, Chris Chukwudi Onochie, and Chris Obaro Obruche. “Lung Cancer Detection Using A Hybridized Contrast-Based Xception Model On Image Data: A Pilot Study.” International Journal of Advanced Computing and Intelligent System 4(1) (2025): online first. https://doi.org/10.66917/ijacis.a000021
Agboi, J.; Onoma, P.; Ojugo, A. A.; Ugbotu, E. V.; Aghaunor, T. C.; Odiakaose, C. C.; Eboka, A. O.; Binitie, A. P.; Ezzeh, P. O.; Ejeh, P. O.; Geteloma, V. O.; Idama, R. O.; Orobor, A. I.; Onochie, C. C.; Obruche, C. O. (2025) ‘Lung Cancer Detection Using A Hybridized Contrast-Based Xception Model On Image Data: A Pilot Study’, International Journal of Advanced Computing and Intelligent System, 4(1), pp. online first. doi:10.66917/ijacis.a000021
BibTeX
@article{msisijacis-a000021,
title = {Lung Cancer Detection Using A Hybridized Contrast-Based Xception Model On Image Data: A Pilot Study},
author = {Joy Agboi and Paul Onoma and Arnold Adim Ojugo and Eferhire Valentine Ugbotu and Tabitha Chukwudi Aghaunor and Chris Chukwufunaya Odiakaose and Andrew Okonji Eboka and Amaka Patience Binitie and Peace Oguguo Ezzeh and Patrick Ogholuwarami Ejeh and Victor Ochuko Geteloma and Rebecca Okeoghene Idama and Anderson Ise Orobor and Chris Chukwudi Onochie and Chris Obaro Obruche},
journal = {International Journal of Advanced Computing and Intelligent System},
year = {2025},
volume = {4},
number = {1},
doi = {10.66917/ijacis.a000021}
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