A Systematic Evaluation of Photometric Data Augmentation Combinations in Medical Object Detection

Onur Erdem Korkmaz

doi:10.31466/kfbd.1870662

TR EN

Tıbbi Nesne Tespitinde Fotometrik Veri Artırma Kombinasyonlarının Sistematik Bir Değerlendirmesi

Abstract

Bu çalışma, farklı veri artırma tekniklerinin tıbbi nesne tespiti performansı üzerindeki etkisini Kvasir-SEG veri seti kullanılarak incelemektedir. Faster R-CNN X101-FPN ve YOLOv7 modelleri temel alınmıştır. Veri artırma yöntemleri görüntü sınıflandırmada yaygın biçimde kullanılmaktadır. Ancak nesne tespiti için yapılan sistematik çalışmalar sınırlıdır. Bunun temel nedeni, her geometrik dönüşüm adımında bounding-box bütünlüğünün korunması gerekliliğidir. Bu çalışmada sekiz fotometrik veri artırma tekniği (Hue, Noise, Saturation, Grayscale, Blur, Brightness, Contrast ve Cutout) bağımsız olarak ve çok seviyeli kombinasyonlar şeklinde uygulanmıştır. Her bir yöntem tekli, ikili, üçlü ve tam kombinasyon yapıları içinde test edilmiştir. Model performansı, COCO değerlendirme standardına göre hesaplanan Ortalama Doğruluk (mAP) metriği ile ölçülmüştür. Elde edilen sonuçlar, renk odaklı artırmaların polip tespiti görevlerinde bozulma odaklı yöntemlere kıyasla daha yüksek doğruluk artışı sağladığını göstermektedir. Ayrıca artırma derinliğinin aşırı yükselmesi, öğrenme sürecini yavaşlatmakta ve doğruluk kazanımlarını engellemektedir. Bu çalışma, tıbbi nesne tespitinde veri artırma çeşitliliğinin etkisini sistematik biçimde ortaya koymakta ve etkili artırma yapılarına yönelik uygulayıcılar için net öneriler sunmaktadır.

Keywords

Derin öğrenme, Bilgisayarlı görü, Veri artırma, Tıbbi nesne tespiti

A Systematic Evaluation of Photometric Data Augmentation Combinations in Medical Object Detection

Abstract

This study examines how different data augmentation strategies influence medical object detection performance on the Kvasir-SEG dataset by using Faster R-CNN X101-FPN and YOLOv7 as benchmark models. Augmentation is widely used to improve robustness in image classification. However, systematic analyses in object detection are still limited because bounding-box integrity must be preserved during every geometric transformation step. In this work, eight photometric augmentation techniques (Hue, Noise, Saturation, Grayscale, Blur, Brightness, Contrast, and Cutout) were applied independently and in multi-level combinations. Each augmentation was tested in single, double, triple, and full pipelines. Model performance was evaluated through mean Average Precision (mAP) using the COCO evaluation standard. The results show that color-based augmentations improve detection accuracy more than distortion-based augmentations in polyp detection tasks. The results also show that excessive augmentation depth slows model convergence and prevents accuracy gains. This study provides a structured analysis of augmentation depth and diversity on a medical object detection dataset and offers clear guidance for designing effective augmentation pipelines for medical object detection.

Keywords

Deep learning, Computer vision, Data augmentation, Medical object detection

Supporting Institution

No

Ethical Statement

The author declares that this study complies with Research and Publication Ethics.

Thanks

The author would like to thank the High Performance Computing Center of Atatürk University for providing the computational resources used in the data analysis of this study.

References

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Details

Primary Language

English

Subjects

Software Engineering (Other), Biomedical Imaging, Biomedical Diagnosis

Journal Section

Research Article

Authors

Onur Erdem Korkmaz ^*
0000-0001-6336-6147
Türkiye

Publication Date

March 26, 2026

Submission Date

January 24, 2026

Acceptance Date

March 25, 2026

Published in Issue

Year 2026 Volume: 16 Number: 2

DOI

https://doi.org/10.31466/kfbd.1870662

IZ

https://izlik.org/JA35PZ49CR

APA

Korkmaz, O. E. (2026). A Systematic Evaluation of Photometric Data Augmentation Combinations in Medical Object Detection. Karadeniz Fen Bilimleri Dergisi, 16(2), 572-591. https://doi.org/10.31466/kfbd.1870662

AMA

1.Korkmaz OE. A Systematic Evaluation of Photometric Data Augmentation Combinations in Medical Object Detection. KFBD. 2026;16(2):572-591. doi:10.31466/kfbd.1870662

Chicago

Korkmaz, Onur Erdem. 2026. “A Systematic Evaluation of Photometric Data Augmentation Combinations in Medical Object Detection”. Karadeniz Fen Bilimleri Dergisi 16 (2): 572-91. https://doi.org/10.31466/kfbd.1870662.

EndNote

Korkmaz OE (March 1, 2026) A Systematic Evaluation of Photometric Data Augmentation Combinations in Medical Object Detection. Karadeniz Fen Bilimleri Dergisi 16 2 572–591.

IEEE

[1]O. E. Korkmaz, “A Systematic Evaluation of Photometric Data Augmentation Combinations in Medical Object Detection”, KFBD, vol. 16, no. 2, pp. 572–591, Mar. 2026, doi: 10.31466/kfbd.1870662.

ISNAD

Korkmaz, Onur Erdem. “A Systematic Evaluation of Photometric Data Augmentation Combinations in Medical Object Detection”. Karadeniz Fen Bilimleri Dergisi 16/2 (March 1, 2026): 572-591. https://doi.org/10.31466/kfbd.1870662.

JAMA

1.Korkmaz OE. A Systematic Evaluation of Photometric Data Augmentation Combinations in Medical Object Detection. KFBD. 2026;16:572–591.

MLA

Korkmaz, Onur Erdem. “A Systematic Evaluation of Photometric Data Augmentation Combinations in Medical Object Detection”. Karadeniz Fen Bilimleri Dergisi, vol. 16, no. 2, Mar. 2026, pp. 572-91, doi:10.31466/kfbd.1870662.

Vancouver

1.Onur Erdem Korkmaz. A Systematic Evaluation of Photometric Data Augmentation Combinations in Medical Object Detection. KFBD. 2026 Mar. 1;16(2):572-91. doi:10.31466/kfbd.1870662

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Tıbbi Nesne Tespitinde Fotometrik Veri Artırma Kombinasyonlarının Sistematik Bir Değerlendirmesi

Abstract

Keywords

A Systematic Evaluation of Photometric Data Augmentation Combinations in Medical Object Detection

Abstract

Keywords

Supporting Institution

Ethical Statement

Thanks

References

Details

Primary Language

Subjects

Journal Section

Authors

Publication Date

Submission Date

Acceptance Date

Published in Issue

DOI

IZ

Cite