DeepInsight-Net: a CBAM-enhanced ResNet50 framework with focal loss for robust cervical cancer classification on multi-center datasets.

Background: Cervical cancer remains one of the leading causes of gynecological mortality worldwide, largely due to the limitations of manual cytological screening, which is time-consuming and susceptible to inter-observer variability. Although deep learning has demonstrated strong potential for automating cervical cytopathology, existing Convolutional Neural Network (CNN) models are hindered by two critical challenges: Methods: To address these limitations, we propose DeepInsight-Net, a robust three-stage deep learning framework for cervical cell classification. The core architecture integrates Convolutional Block Attention Modules (CBAM) into a ResNet50 backbone to enhance spatial and channel-wise feature discrimination, enabling the network to selectively emphasize nuclear and nuclear-cytoplasmic boundary regions while suppressing irrelevant background noise. To further mitigate class imbalance, the conventional cross-entropy loss is replaced with Focal Loss, which dynamically down-weights easily classified samples and prioritizes hard, misclassified instances during training. Results: Extensive experiments conducted on the benchmark SiPaKMeddataset demonstrate that DeepInsight-Net achieves a state-of-the-art classification accuracy of 99.63%, outperforming 15 competitive deep learning models, including EfficientNet-B6 and DenseNet169. Moreover, cross-dataset generalization experiments on an independent Liquid-Based Cytology (LBC) dataset yield an accuracy of 98.62%, confirming the robustness and domain adaptability of the proposed framework. Visual interpretability analyses using Grad-CAM and t-SNE reveal that the model consistently focuses on biologically relevant cellular regions, supporting the reliability of its predictions. Conclusion: The proposed DeepInsight-Net effectively addresses spatial irrelevance and class imbalance in cervical cytology analysis through attention-guided feature learning and loss re-weighting. The strong performance across multiple datasets, combined with transparent visual explainability, highlights its potential as a reliable computer-aided diagnosis (CAD) tool for supporting cervical cancer screening in real-world clinical settings.