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

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.