Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types

Duygu Cakır

doi:10.35377/saucis...1771175

Research Article

Year 2025, Issue: Advanced Online Publication, 785 - 797

Duygu Cakır

https://doi.org/10.35377/saucis...1771175

Abstract

References

H. Borgli et al., “HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy,” Scientific Data, vol. 7, no. 1, Aug. 2020, doi: 10.1038/s41597-020-00622-y.
F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, "Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries," CA: A Cancer Journal for Clinicians, vol. 68, no. 6, pp. 394–424, Nov. 2018, doi: 10.3322/caac.21492.
K. Gono et al., "Appearance of enhanced tissue features in narrow-band endoscopic imaging," Journal of Biomedical Optics, vol. 9, no. 3, pp. 568–577, 2004, doi: 10.1117/1.1695563.
Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, "Deep learning for visual understanding: A review," Neurocomputing, vol. 187, pp. 27–48, 2016, doi: 10.1016/j.neucom.2015.09.116.
A. G. Howard et al., "MobileNets: Efficient convolutional neural networks for mobile vision applications," arXiv preprint arXiv:1704.04861, 2017.
T. Hirasawa et al., "Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images," Gastric Cancer, vol. 21, no. 4, pp. 653–660, 2018, doi: 10.1007/s10120-018-0793-2.
T. Islam, M. S. Hafiz, J. R. Jim, M. M. Kabir, and M. F. Mridha, "A systematic review of deep learning data augmentation in medical imaging: Recent advances and future research directions," Healthcare Analytics, vol. 5, p. 100340, Jun. 2024, doi: 10.1016/j.health.2024.100340.
A. Kebaili, J. Lapuyade-Lahorgue, and S. Ruan, "Deep learning approaches for data augmentation in medical imaging: A review," Journal of Imaging, vol. 9, no. 4, p. 81, 2023, doi: 10.3390/jimaging9040081.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Advances in Neural Information Processing Systems (NIPS), vol. 25, 2012, pp. 1097–1105.
M. W. Lafarge, J. P. W. Pluim, K. A. J. Eppenhof, P. Moeskops, and M. Veta, "Domain-adversarial neural networks to address the appearance variability of histopathology images," in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (DLMIA), Cham: Springer, 2017, pp. 83–91, doi: 10.1007/978-3-319-67558-9_10.
G. Litjens et al., "A survey on deep learning in medical image analysis," Medical Image Analysis, vol. 42, pp. 60–88, Dec. 2017, doi: 10.1016/j.media.2017.07.005.
J. Nalepa, M. Marcinkiewicz, and M. Kawulok, "Data augmentation for brain-tumor segmentation: A review," Frontiers in Computational Neuroscience, vol. 13, p. 83, Dec. 2019, doi: 10.3389/fncom.2019.00083.
K. Pogorelov et al., "Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection," in Proceedings of the 8th ACM on Multimedia Systems Conference (MMSys '17), Taipei, Taiwan, 2017, pp. 164–169, doi: 10.1145/3083187.3083212.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 779–788, doi: 10.1109/CVPR.2016.91.
A. Sharma, R. Kumar, and P. Garg, "Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images," International Journal of Medical Informatics, vol. 177, p. 105142, Sep. 2023, doi: 10.1016/j.ijmedinf.2023.105142.
C. Shorten and T. M. Khoshgoftaar, "A survey on image data augmentation for deep learning," Journal of Big Data, vol. 6, no. 1, pp. 1–48, 2019, doi: 10.1186/s40537-019-0197-0.
R. L. Siegel, A. N. Giaquinto, and A. Jemal, "Cancer statistics, 2024," CA: A Cancer Journal for Clinicians, vol. 74, no. 1, pp. 12–49, Jan. 2024, doi: 10.3322/caac.21820.
K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
Z. Sobhaninia, N. Abharian, N. Karimi, S. Shirani, and S. Samavi, "Endoscopy classification model using swin transformer and saliency map," arXiv preprint arXiv:2303.06736, 2023.
D. Tellez et al., "Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology," Medical Image Analysis, vol. 58, p. 101544, Dec. 2019, doi: 10.1016/j.media.2019.101544.
G. Urban et al., "Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy," Gastroenterology, vol. 155, no. 4, pp. 1069–1078, Oct. 2018, doi: 10.1053/j.gastro.2018.06.037.
J. Wu and Y. Gu, "Unleashing the power of depth and pose estimation neural networks by designing compatible endoscopic images," arXiv preprint arXiv:2309.07390, 2023.
M. Xu, S. Yoon, A. Fuentes, and D. S. Park, "A comprehensive survey of image augmentation techniques for deep learning," Pattern Recognition, vol. 137, p. 109347, May 2023, doi: 10.1016/j.patcog.2023.109347.
J. Zhang et al., "Deep learning model based on endoscopic images predicting treatment response in locally advanced rectal cancer undergo neoadjuvant chemoradiotherapy: A multicenter study," Journal of Cancer Research and Clinical Oncology, vol. 150, no. 7, p. 350, Jul. 2024, doi: 10.1007/s00432-024-05876-2.
F. Chollet, "Xception: Deep learning with depthwise separable convolutions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 1251–1258, doi: 10.1109/CVPR.2017.195.
D. Cakir and N. Arica, "Cascading CNNs for facial action unit detection," Engineering Science and Technology, an International Journal, vol. 47, p. 101553, Nov. 2023, doi: 10.1016/j.jestch.2023.101553.
Z. Nie, M. Xu, Z. Wang, X. Lu, and W. Song, "A review of application of deep learning in endoscopic image processing," Journal of Imaging, vol. 10, no. 11, p. 275, Nov. 2024, doi: 10.3390/jimaging10110275.
M. N. Noor, M. Nazir, S. A. Khan, O. Y. Song, and I. Ashraf, "Efficient gastrointestinal disease classification using pretrained deep convolutional neural network," Electronics, vol. 12, no. 7, p. 1557, Mar. 2023, doi: 10.3390/electronics12071557.
A. Lateef et al., "Comparative analysis of color space in histopathology image classification," Jurnal Kejuruteraan (Journal of Engineering), vol. 37, no. 2, pp. 617–634, 2025, doi: 10.17576/jkukm-2025-37(2)-06.
S. Biswas et al., "Which color channel is better for diagnosing retinal diseases automatically in color fundus photographs?" Life, vol. 12, no. 7, p. 973, Jul. 2022, doi: 10.3390/life12070973.
A. Asperti and C. Mastronardo, "The effectiveness of data augmentation for detection of gastrointestinal diseases from endoscopical images," arXiv preprint arXiv:1712.03689, 2017.
D. Shen, G. Wu, and H.-I. Suk, "Deep learning in medical image analysis," Annual Review of Biomedical Engineering, vol. 19, pp. 221–248, Jun. 2017, doi: 10.1146/annurev-bioeng-071516-044442.
N. Tajbakhsh et al., "Convolutional neural networks for medical image analysis: Full training or fine tuning?" IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1299–1312, May 2016, doi: 10.1109/TMI.2016.2535302.
S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345–1359, Oct. 2010, doi: 10.1109/TKDE.2009.191.
R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, "Convolutional neural networks: An overview and application in radiology," Insights into Imaging, vol. 9, no. 4, pp. 611–629, Aug. 2018, doi: 10.1007/s13244-018-0639-9.
M. Raghu, C. Zhang, J. Kleinberg, and S. Bengio, "Transfusion: Understanding transfer learning for medical imaging," in Advances in Neural Information Processing Systems (NeurIPS), vol. 32, 2019, pp. 3347–3357.
X. Jiang, Z. Hu, S. Wang, and Y. Zhang, "Deep learning for medical image-based cancer diagnosis," Cancers, vol. 15, no. 14, p. 3608, Jul. 2023, doi: 10.3390/cancers15143608.
P. K. Mall et al., "A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities," Healthcare Analytics, vol. 4, p. 100216, Dec. 2023, doi: 10.1016/j.health.2023.100216.
E. Goceri, "Medical image data augmentation: Techniques, comparisons and interpretations," Artificial Intelligence Review, vol. 56, no. 11, pp. 12561–12605, Nov. 2023, doi: 10.1007/s10462-023-10453-z.
M. Cossio, "Augmenting medical imaging: A comprehensive catalogue of 65 techniques for enhanced data analysis," arXiv preprint arXiv:2303.01178, 2023.
X. Qi et al., "MediAug: Exploring visual augmentation in medical imaging," in Annual Conference on Medical Image Understanding and Analysis (MIUA), Cham: Springer Nature Switzerland, 2024, pp. 218–232, doi: 10.1007/978-3-031-98688-8_16.
E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, "RandAugment: Practical automated data augmentation with a reduced search space," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 702–703, doi: 10.1109/CVPRW50498.2020.00359.
R. Ghnemat and S. Al-Mashaqbeh, "Novel image data augmentation technique for deep learning using least significant bit encryption," in Proceedings of the 9th International Conference on Machine Learning Technologies (ICMLT), 2024, pp. 143–152, doi: 10.1145/3674029.3674053.
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, "AutoAugment: Learning augmentation policies from data," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 113–123, doi: 10.1109/CVPR.2019.00020.
S. Lim, I. Kim, T. Kim, C. Kim, and S. Kim, "Fast AutoAugment," in Advances in Neural Information Processing Systems (NeurIPS), vol. 32, 2019, pp. 6665–6675.

Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types

Year 2025, Issue: Advanced Online Publication, 785 - 797

Duygu Cakır

https://doi.org/10.35377/saucis...1771175

Abstract

Deep learning has significantly advanced medical image analysis, particularly in the field of endoscopic images. However, these advancements are constrained by the availability of high-quality, annotated medical datasets. This study examines the effect of selective RGB channel jittering as a targeted data augmentation strategy to improve multi-pathological disease detection in endoscopic images. The proposed approach applies channel-specific Gaussian noise to individual RGB channels, implements transfer learning using two different architectures, and evaluates performance across four gastrointestinal conditions: erosion, polyp, tumor, and ulcer from the MedFMC dataset. to prove robustness. The results demonstrate that blue channel jittering consistently improves detection performance by up to 2.7% in accuracy and 3.05% in F1 across all pathologies, while red and green channel jittering significantly degrade performance. This degradation when jittering red and green channels indicates that these channels contain critical discriminative information for gastrointestinal pathology detection, while blue channel enhancement acts as effective regularization. These findings offer important insights for developing targeted data augmentation strategies in medical image analysis.

Keywords

Medical Image Analysis , Endoscopic Disease Detection , Data Augmentation , Channel Jittering , Transfer Learning

References

H. Borgli et al., “HyperKvasir, a comprehensive multi-class image and video dataset for gastrointestinal endoscopy,” Scientific Data, vol. 7, no. 1, Aug. 2020, doi: 10.1038/s41597-020-00622-y.
F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, "Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries," CA: A Cancer Journal for Clinicians, vol. 68, no. 6, pp. 394–424, Nov. 2018, doi: 10.3322/caac.21492.
K. Gono et al., "Appearance of enhanced tissue features in narrow-band endoscopic imaging," Journal of Biomedical Optics, vol. 9, no. 3, pp. 568–577, 2004, doi: 10.1117/1.1695563.
Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, "Deep learning for visual understanding: A review," Neurocomputing, vol. 187, pp. 27–48, 2016, doi: 10.1016/j.neucom.2015.09.116.
A. G. Howard et al., "MobileNets: Efficient convolutional neural networks for mobile vision applications," arXiv preprint arXiv:1704.04861, 2017.
T. Hirasawa et al., "Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images," Gastric Cancer, vol. 21, no. 4, pp. 653–660, 2018, doi: 10.1007/s10120-018-0793-2.
T. Islam, M. S. Hafiz, J. R. Jim, M. M. Kabir, and M. F. Mridha, "A systematic review of deep learning data augmentation in medical imaging: Recent advances and future research directions," Healthcare Analytics, vol. 5, p. 100340, Jun. 2024, doi: 10.1016/j.health.2024.100340.
A. Kebaili, J. Lapuyade-Lahorgue, and S. Ruan, "Deep learning approaches for data augmentation in medical imaging: A review," Journal of Imaging, vol. 9, no. 4, p. 81, 2023, doi: 10.3390/jimaging9040081.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," in Advances in Neural Information Processing Systems (NIPS), vol. 25, 2012, pp. 1097–1105.
M. W. Lafarge, J. P. W. Pluim, K. A. J. Eppenhof, P. Moeskops, and M. Veta, "Domain-adversarial neural networks to address the appearance variability of histopathology images," in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support (DLMIA), Cham: Springer, 2017, pp. 83–91, doi: 10.1007/978-3-319-67558-9_10.
G. Litjens et al., "A survey on deep learning in medical image analysis," Medical Image Analysis, vol. 42, pp. 60–88, Dec. 2017, doi: 10.1016/j.media.2017.07.005.
J. Nalepa, M. Marcinkiewicz, and M. Kawulok, "Data augmentation for brain-tumor segmentation: A review," Frontiers in Computational Neuroscience, vol. 13, p. 83, Dec. 2019, doi: 10.3389/fncom.2019.00083.
K. Pogorelov et al., "Kvasir: A multi-class image dataset for computer aided gastrointestinal disease detection," in Proceedings of the 8th ACM on Multimedia Systems Conference (MMSys '17), Taipei, Taiwan, 2017, pp. 164–169, doi: 10.1145/3083187.3083212.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 779–788, doi: 10.1109/CVPR.2016.91.
A. Sharma, R. Kumar, and P. Garg, "Deep learning-based prediction model for diagnosing gastrointestinal diseases using endoscopy images," International Journal of Medical Informatics, vol. 177, p. 105142, Sep. 2023, doi: 10.1016/j.ijmedinf.2023.105142.
C. Shorten and T. M. Khoshgoftaar, "A survey on image data augmentation for deep learning," Journal of Big Data, vol. 6, no. 1, pp. 1–48, 2019, doi: 10.1186/s40537-019-0197-0.
R. L. Siegel, A. N. Giaquinto, and A. Jemal, "Cancer statistics, 2024," CA: A Cancer Journal for Clinicians, vol. 74, no. 1, pp. 12–49, Jan. 2024, doi: 10.3322/caac.21820.
K. Simonyan and A. Zisserman, "Very deep convolutional networks for large-scale image recognition," arXiv preprint arXiv:1409.1556, 2014.
Z. Sobhaninia, N. Abharian, N. Karimi, S. Shirani, and S. Samavi, "Endoscopy classification model using swin transformer and saliency map," arXiv preprint arXiv:2303.06736, 2023.
D. Tellez et al., "Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology," Medical Image Analysis, vol. 58, p. 101544, Dec. 2019, doi: 10.1016/j.media.2019.101544.
G. Urban et al., "Deep learning localizes and identifies polyps in real time with 96% accuracy in screening colonoscopy," Gastroenterology, vol. 155, no. 4, pp. 1069–1078, Oct. 2018, doi: 10.1053/j.gastro.2018.06.037.
J. Wu and Y. Gu, "Unleashing the power of depth and pose estimation neural networks by designing compatible endoscopic images," arXiv preprint arXiv:2309.07390, 2023.
M. Xu, S. Yoon, A. Fuentes, and D. S. Park, "A comprehensive survey of image augmentation techniques for deep learning," Pattern Recognition, vol. 137, p. 109347, May 2023, doi: 10.1016/j.patcog.2023.109347.
J. Zhang et al., "Deep learning model based on endoscopic images predicting treatment response in locally advanced rectal cancer undergo neoadjuvant chemoradiotherapy: A multicenter study," Journal of Cancer Research and Clinical Oncology, vol. 150, no. 7, p. 350, Jul. 2024, doi: 10.1007/s00432-024-05876-2.
F. Chollet, "Xception: Deep learning with depthwise separable convolutions," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 2017, pp. 1251–1258, doi: 10.1109/CVPR.2017.195.
D. Cakir and N. Arica, "Cascading CNNs for facial action unit detection," Engineering Science and Technology, an International Journal, vol. 47, p. 101553, Nov. 2023, doi: 10.1016/j.jestch.2023.101553.
Z. Nie, M. Xu, Z. Wang, X. Lu, and W. Song, "A review of application of deep learning in endoscopic image processing," Journal of Imaging, vol. 10, no. 11, p. 275, Nov. 2024, doi: 10.3390/jimaging10110275.
M. N. Noor, M. Nazir, S. A. Khan, O. Y. Song, and I. Ashraf, "Efficient gastrointestinal disease classification using pretrained deep convolutional neural network," Electronics, vol. 12, no. 7, p. 1557, Mar. 2023, doi: 10.3390/electronics12071557.
A. Lateef et al., "Comparative analysis of color space in histopathology image classification," Jurnal Kejuruteraan (Journal of Engineering), vol. 37, no. 2, pp. 617–634, 2025, doi: 10.17576/jkukm-2025-37(2)-06.
S. Biswas et al., "Which color channel is better for diagnosing retinal diseases automatically in color fundus photographs?" Life, vol. 12, no. 7, p. 973, Jul. 2022, doi: 10.3390/life12070973.
A. Asperti and C. Mastronardo, "The effectiveness of data augmentation for detection of gastrointestinal diseases from endoscopical images," arXiv preprint arXiv:1712.03689, 2017.
D. Shen, G. Wu, and H.-I. Suk, "Deep learning in medical image analysis," Annual Review of Biomedical Engineering, vol. 19, pp. 221–248, Jun. 2017, doi: 10.1146/annurev-bioeng-071516-044442.
N. Tajbakhsh et al., "Convolutional neural networks for medical image analysis: Full training or fine tuning?" IEEE Transactions on Medical Imaging, vol. 35, no. 5, pp. 1299–1312, May 2016, doi: 10.1109/TMI.2016.2535302.
S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Transactions on Knowledge and Data Engineering, vol. 22, no. 10, pp. 1345–1359, Oct. 2010, doi: 10.1109/TKDE.2009.191.
R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, "Convolutional neural networks: An overview and application in radiology," Insights into Imaging, vol. 9, no. 4, pp. 611–629, Aug. 2018, doi: 10.1007/s13244-018-0639-9.
M. Raghu, C. Zhang, J. Kleinberg, and S. Bengio, "Transfusion: Understanding transfer learning for medical imaging," in Advances in Neural Information Processing Systems (NeurIPS), vol. 32, 2019, pp. 3347–3357.
X. Jiang, Z. Hu, S. Wang, and Y. Zhang, "Deep learning for medical image-based cancer diagnosis," Cancers, vol. 15, no. 14, p. 3608, Jul. 2023, doi: 10.3390/cancers15143608.
P. K. Mall et al., "A comprehensive review of deep neural networks for medical image processing: Recent developments and future opportunities," Healthcare Analytics, vol. 4, p. 100216, Dec. 2023, doi: 10.1016/j.health.2023.100216.
E. Goceri, "Medical image data augmentation: Techniques, comparisons and interpretations," Artificial Intelligence Review, vol. 56, no. 11, pp. 12561–12605, Nov. 2023, doi: 10.1007/s10462-023-10453-z.
M. Cossio, "Augmenting medical imaging: A comprehensive catalogue of 65 techniques for enhanced data analysis," arXiv preprint arXiv:2303.01178, 2023.
X. Qi et al., "MediAug: Exploring visual augmentation in medical imaging," in Annual Conference on Medical Image Understanding and Analysis (MIUA), Cham: Springer Nature Switzerland, 2024, pp. 218–232, doi: 10.1007/978-3-031-98688-8_16.
E. D. Cubuk, B. Zoph, J. Shlens, and Q. V. Le, "RandAugment: Practical automated data augmentation with a reduced search space," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 702–703, doi: 10.1109/CVPRW50498.2020.00359.
R. Ghnemat and S. Al-Mashaqbeh, "Novel image data augmentation technique for deep learning using least significant bit encryption," in Proceedings of the 9th International Conference on Machine Learning Technologies (ICMLT), 2024, pp. 143–152, doi: 10.1145/3674029.3674053.
E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, "AutoAugment: Learning augmentation policies from data," in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019, pp. 113–123, doi: 10.1109/CVPR.2019.00020.
S. Lim, I. Kim, T. Kim, C. Kim, and S. Kim, "Fast AutoAugment," in Advances in Neural Information Processing Systems (NeurIPS), vol. 32, 2019, pp. 6665–6675.

There are 45 citations in total.

Details

Primary Language	English
Subjects	Computing Applications in Health, Software Engineering (Other)
Journal Section	Research Article
Authors	Duygu Cakır 0000-0003-1600-3989
Submission Date	August 23, 2025
Acceptance Date	October 19, 2025
Early Pub Date	December 11, 2025
Published in Issue	Year 2025 Issue: Advanced Online Publication

Cite

APA	Cakır, D. (2025). Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types. Sakarya University Journal of Computer and Information Sciences(Advanced Online Publication), 785-797. https://doi.org/10.35377/saucis...1771175
AMA	Cakır D. Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types. SAUCIS. December 2025;(Advanced Online Publication):785-797. doi:10.35377/saucis.1771175
Chicago	Cakır, Duygu. “Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types”. Sakarya University Journal of Computer and Information Sciences, no. Advanced Online Publication (December 2025): 785-97. https://doi.org/10.35377/saucis. 1771175.
EndNote	Cakır D (December 1, 2025) Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types. Sakarya University Journal of Computer and Information Sciences Advanced Online Publication 785–797.
IEEE	D. Cakır, “Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types”, SAUCIS, no. Advanced Online Publication, pp. 785–797, December2025, doi: 10.35377/saucis...1771175.
ISNAD	Cakır, Duygu. “Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types”. Sakarya University Journal of Computer and Information Sciences Advanced Online Publication (December2025), 785-797. https://doi.org/10.35377/saucis. 1771175.
JAMA	Cakır D. Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types. SAUCIS. 2025;:785–797.
MLA	Cakır, Duygu. “Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types”. Sakarya University Journal of Computer and Information Sciences, no. Advanced Online Publication, 2025, pp. 785-97, doi:10.35377/saucis. 1771175.
Vancouver	Cakır D. Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types. SAUCIS. 2025(Advanced Online Publication):785-97.

Article Files

Full Text

INDEXING & ABSTRACTING & ARCHIVING

29070 The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License