A Hybrid CNN-BiGRU-GAN Framework for Enhanced Automated Analysis of Cervical Cancer in Medical Imaging

Abstract

Cervical cancer screening requires reliable automated systems capable of overcoming variability in staining, morphology, and limited annotated data, which often undermine the performance of traditional machine learning and deep learning approaches. Existing techniques commonly rely on single-modality feature extraction or static fusion, resulting in weak generalization, class imbalance sensitivity, and limited interpretability in clinical environments. Addressing these gaps, the research introduces DiagnoFusionNet, a hybrid CNN-BiGRU-GAN framework that integrates spatial features from Convolutional Neural Network (CNN), contextual dependencies from Bidirectional Gated Recurrent Unit (BiGRU), and Generative Adversarial Network (GAN)-generated samples to enhance data diversity and correct minority-class deficiencies. The methodology incorporates an Adaptive Triple-Stage Feature Fusion mechanism that dynamically recalibrates modality contributions using discriminator-informed attention, ensuring discriminative and clinically aligned feature representations. Experiments on the SIPaKMeD dataset demonstrate strong performance with 97.89% accuracy, 97.69% precision, 96.95% recall, 96.89% F1-score, and a 0.99 AUC, supported by GAN evaluation metrics, including an FID of 18.3, IS of 3.91, and SSIM of 0.92. Ablation analysis confirms the dominant contribution of the adaptive fusion module, while t-SNE clustering and confusion-matrix inspection highlight effective separability and reduced misclassification. Model development and experimentation were executed using Python, TensorFlow, Keras, OpenCV, and Scikitlearn on GPU-enabled environments. The framework provides a clinically interpretable, data-efficient, and scalable solution for automated cervical cytology analysis in real-world and resource-limited settings.