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

Duygu Cakır

doi:10.35377/saucis...1771175

EN

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

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

References

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Details

Primary Language

English

Subjects

Computing Applications in Health , Software Engineering (Other)

Journal Section

Research Article

Authors

Duygu Cakır ^*
0000-0003-1600-3989
Türkiye

Early Pub Date

December 11, 2025

Publication Date

December 29, 2025

Submission Date

August 23, 2025

Acceptance Date

October 19, 2025

Published in Issue

Year 2025 Volume: 8 Number: 4

DOI

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

IZ

https://izlik.org/JA76SC26ZU

Cite

RIS / Bibtex

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, 8(4), 785-797. https://doi.org/10.35377/saucis...1771175

AMA

1.Cakır D. Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types. SAUCIS. 2025;8(4):785-797. doi:10.35377/saucis.1771175

Chicago

Cakır, Duygu. 2025. “Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types”. Sakarya University Journal of Computer and Information Sciences 8 (4): 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 8 4 785–797.

IEEE

[1]D. Cakır, “Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types”, SAUCIS, vol. 8, no. 4, pp. 785–797, Dec. 2025, 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 8/4 (December 1, 2025): 785-797. https://doi.org/10.35377/saucis. 1771175.

JAMA

1.Cakır D. Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types. SAUCIS. 2025;8:785–797.

MLA

Cakır, Duygu. “Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 4, Dec. 2025, pp. 785-97, doi:10.35377/saucis. 1771175.

Vancouver

1.Duygu Cakır. Selective RGB Channel Jittering for Robust Endoscopic Disease Detection Across Multiple Lesion Types. SAUCIS. 2025 Dec. 1;8(4):785-97. doi:10.35377/saucis. 1771175