Deep learning based detection of endometriosis lesions in laparoscopic images with 5-fold cross-validation

Shujaat Ali Zaidi, Varin Chouvatut, Chailert Phongnarisorn, Dussadee Praserttitipong
In: Intelligence-Based Medicine · 2025 · vol. 11 , pp. 100230 · doi:10.1016/j.ibmed.2025.100230 · W4408252086
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This study developed and evaluated deep learning models (VGG19, ResNet50, Inception V3) with 5-fold cross-validation and data augmentation to accurately classify endometriosis lesions from laparoscopic images, with Inception V3 achieving the highest accuracy of 0.93.

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Abstract

Endometriosis, a complex gynecological condition, presents significant diagnostic challenges due to the subtle and varied appearance of its lesions. This study leverages deep learning to classify endometriosis lesions in laparoscopic images using the Gynecologic Laparoscopy Endometriosis Dataset (GLENDA). Three deep learning models—VGG19, ResNet50, and Inception V3—were trained and evaluated with 5-fold cross-validation to enhance generalizability and mitigate overfitting. Robust data augmentation techniques were applied to address dataset limitations. The models were tasked with classifying lesions into pathological and nonpathological categories. Experimental results demonstrated strong performance, with VGG19, ResNet50, and Inception V3 achieving accuracies of 0.89, 0.91, and 0.93, respectively. Inception V3 outperformed the others, highlighting its efficacy for this task. The findings underscore the potential of deep learning in improving endometriosis diagnosis, offering a reliable tool for clinicians. This study contributes to the growing field of AI-driven medical image analysis, emphasizing the value of cross-validation and data augmentation in enhancing model performance for specialized medical applications. • In this study, we proposed deep learning models, VGG19, ResNet50, and Inception V3, to classify endometriosis lesions from laparoscopic images. • We proposed the 5-fold cross-validation method on each model, namely VGG19, ResNet50, and Inception V3. • Effective data augmentation approaches (Flipping, Rotation, Shearing, Zooming) are proposed for endometriosis lesion classification.

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Condition tags

endometriosis

Citation neighborhood (2-hop)

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. Outer rings show 2-hop neighbours — papers reached through the immediate citers/citees. [ collapse to 1-hop ]

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