DCGAN-based Cytology Image Augmentation for Cervical Cancer Cell Classification Using Transfer Learning
Betelhem Zewdu Wubineh, Łukasz Jeleń, Andrzej Rusiecki · Procedia Computer Science · 2025
Cervical cancer is the fourth most common cancer among women worldwide. This study aims to classify cervical cancer cell images using deep learning techniques. Given the limited dataset, we propose a Deep Convolutional Generative Adversarial Network (DCGAN) to generate synthetic images and improve classification performance. The Pap smear images collected from Pomeranian Medical University were used to evaluate our approach. Several deep neural architectures were applied for image classification, including CNN, VGG16, MobileNet, ResNet50V2, InceptionV3, and Xception. Three experiments were conducted: (1) using the real dataset, (2) combining real and generated datasets, and (3) training with 80% real and generated images while testing on 20% real images not used in generation. In the third experiment, the model achieved accuracies of 96% for Xception and MobileNet, and 94% for ResNet50V2. These results demonstrate that DCGAN-based augmentation significantly improves classification performance and can play a crucial role in aiding the early detection of cervical cancer, enhancing diagnostic accuracy in clinical practice. Overall, this approach effectively addresses dataset limitations and boosts model accuracy for cervical cancer detection.