Endometriosis Lesion Classification Using Deep Transfer Learning Techniques
Shujaat Ali Zaidi, Varin Chouvatut, Chailert Phongnarisorn · International Journal of Advanced Computer Science and Applications · 2025
In resource-limited settings, assisting physicians with disease identification can significantly improve patient outcomes. Early diagnosis is crucial, as many patients could remain healthy with timely intervention. Recent advancements in deep learning models for medical image processing have enabled algorithms to achieve diagnostic accuracy comparable to that of healthcare professionals. This research aims to develop a comprehensive system for the rapid and precise detection of endometriosis lesions. We explore the several deep transfer learning architectures, specifically MobileNetV2, VGG19, and InceptionV3, on the Gynecologic Laparoscopy Endometriosis Dataset (GLENDA). Through extensive literature review and parameter optimization, we find that MobileNetV2 outperforms the other models in terms of accuracy. However, challenges remain, as healthcare imaging datasets often suffer from limited sample sizes and uneven class distributions. Collecting additional samples can be costly and time-consuming, which is a prevalent issue in medical imaging. To address this, we employ Deep convolutional Generative Adversarial Networks (DCGAN) to enhance the dataset by generating synthetic images, thus improving class balance. This image augmentation strategy not only boosts model performance but also reduces the manual effort required for image labeling. We evaluate our proposed model using metrics such as accuracy, precision, recall, and F1-score. Initially, our model achieves an accuracy of 95%. The introduction of synthetic samples results in an increased accuracy of 99%, reflecting a 4%improvement and enhancing the model’s overall efficacy.