Endometriosis is a chronic and painful disorder that significantly affects many aspects of a woman’s life. Its complex symptomatology makes early diagnosis and effective treatment particularly challenging. Although deep Convolutional Neural Networks (CNNs) have shown promise in medical image classification, few studies have explored their application to endometriosis detection. This study evaluates and compares the performance of three advanced CNN models (DesNet121, InceptionV3, and Xception) using laparoscopic images to identify endometriotic tissue. A custom dataset was compiled by merging images from the ENDI and GLENDA databases, with preprocessing steps including normalization and data augmentation. The models were trained and validated using stratified splits, and assessed based on standard metrics (accuracy, precision, recall, AUC, and confusion matrices). The results revealed accuracy scores of 89%, 91%, and 97% for ResNet121, InceptionV3, and Xception, respectively, with Xception demonstrating the highest performance. This approach offers a potential tool for clinicians, aiming to accelerate diagnosis, reduce the rate of misdiagnosis, and improve patient outcomes.