A Hybrid Deep Learning Framework for High‐Precision and Efficient Cervical Cancer Detection

ABSTRACT Cervical cancer remains one of the leading causes of cancer‐related mortality among women worldwide, emphasising the need for early and accurate diagnosis. Traditional deep learning approaches for cervical cancer detection often suffer from low accuracy, limited generalisation, and high computational complexity. To overcome these challenges, this study introduces CerviScan‐Net, a novel hybrid deep learning framework that integrates the Shifted Window Transformer (Swin Transformer), EfficientNet, and bidirectional long short‐term memory (BiLSTM)‐Attention modules for precise and efficient cervical cell classification. The Swin Transformer captures global contextual features, EfficientNet extracts fine‐grained local representations with optimised computational efficiency, and the BiLSTM‐Attention mechanism enhances spatial–semantic learning by focusing on diagnostically significant regions. Interpretability is achieved through a hybrid visualisation strategy combining enhanced gradient‐weighted class activation mapping (Grad‐CAM++; for convolutional layers) and attention rollout (for transformer layers), enabling transparent model predictions. The framework was trained and evaluated on the publicly available SipakMed cervical cell dataset, encompassing five cytological categories – dyskeratotic, koilocytotic, metaplastic, parabasal, and superficial–intermediate cells. Comparative analysis against benchmark models, including ResNet‐50 + LSTM, visual geometry group network (VGG)‐16 + LSTM, Xception (extreme inception network), and DenseNet, demonstrated that CerviScan‐Net achieved superior performance with 98.5% accuracy, 96.3% sensitivity, and 99.2% specificity. These results affirm CerviScan‐Net's robustness, clinical interpretability, and potential as a reliable AI‐assisted diagnostic tool for early detection and screening of cervical cancer.