A Deep Learning-Based Pipeline for Feature Extraction and Segmentation of Endometriosis Stages: A Comparative Study of Transfer Learning and CDGAN Models

Top of Form This study proposes a deep learning-driven approach for extracting features and segmenting various stages of endometriosis from ultrasound images. The proposed pipeline integrates transfer learning with pretrained convolutional neural networks (CNNs) and conditional generative adversarial networks (CDGANs) to improve the accuracy and interpretability of the segmentation process. ResNet and DenseNet models are used in transfer learning to fine-tune pre-trained networks that classify the stages of endometriosis, and the performance of the model is improved by applying CDGAN on the dataset through data augmentation. From the comparison, the CDGAN-based method is more accurate and easier to interpret than the transfer learning model, so it is the preferred method for automatic staging of endometriosis. The results show improved accuracy (90%) and a higher F1-score (0.88), with CDGAN delivering the best segmentation results even in the most complex examples. Automating this portion of medical imaging for endometriosis has the potential to result in more informed treatment choices.