Research on Ovarian Cancer Image Classification Model Based on EfficientNet-B0 and Generalized Mean Pooling

In ovarian cancer image classification, accurate advance prediction is essential for diagnosis and treatment. A model combining EfficientNet-B0 and Generalized Mean (GeM) Pooling is proposed in this paper. The main goal of the model is to capture tiny features and changes in ovarian cancer images and use these features to divide the images into five subtypes, thus enabling advanced prediction and accurate diagnosis of ovarian cancer. The experimental results highlight the variation in the model's performance at different epochs, clearly showing when the model is at its best. This comprehensive approach provides a comprehensive solution for the classification of ovarian cancer subtypes and demonstrates the potential to integrate different neural network architectures, further deepening our understanding of ovarian cancer images. Thus, the study makes an important contribution to the field of ovarian cancer subtype classification, validating the effectiveness of integrating GeM to improve model performance.