Quantitative Analysis of Deep Learning Frameworks with Integrated Feature Fusion for Cervical Cancer Classification and Detection

Abstract

Cervical cancer remains a significant global health concern, necessitating advanced diagnostic tools for early detection and classification. This research presents a quantitative analysis of deep learning frameworks enhanced with integrated feature fusion techniques for improved cervical cancer classification and detection. The study evaluates the performance of state-of-the-art models, such as CNNs and hybrid architectures, by leveraging multi-modal data and feature fusion strategies to enhance accuracy and robustness. Experimental results on benchmark datasets demonstrate the efficacy of the proposed approach in improving diagnostic precision compared to traditional methods. The findings highlight the potential of feature fusion in deep learning for optimizing cervical cancer screening, aiding clinical decision-making, and reducing diagnostic variability.