Deep Learning and Optimization Approaches in Semantic Segmentation and Classification for Ovarian Cancer Detection Using EfficientNetB0 with FPN and Causal Dilated Convolutional Neural Networks: A Review

Abstract

Ovarian cancer remains one of the most lethal gynecological malignancies due to its asymptomatic progression in early stages and frequent late diagnosis, which significantly reduces survival rates. Recent advancements in medical imaging and artificial intelligence have facilitated the development of automated systems for early detection and accurate classification of ovarian cancer. Deep learning techniques, particularly convolutional neural networks, have shown remarkable potential in enhancing diagnostic performance through effective segmentation and classification of histopathological and radiological images. This review highlights advanced architectures such as EfficientNetB0, Feature Pyramid Networks, and causal dilated convolutional neural networks for ovarian cancer detection. EfficientNetB0 is recognized for its ability to extract rich features with lower computational cost, making it suitable for complex medical imaging tasks. Feature Pyramid Networks improve segmentation accuracy by integrating multi-scale feature representations, enabling precise localization of tumor regions. Causal dilated convolutional networks enhance contextual understanding by capturing long-range spatial dependencies without increasing computational burden. The integration of these models forms a hybrid framework capable of performing both segmentation and classification efficiently. Comparative analysis indicates that such hybrid approaches outperform traditional methods in accuracy and efficiency, although challenges like limited annotated data, model interpretability, and computational demands persist, highlighting the need for further research in explainable and efficient AI systems.