CerviDBF-Net: Leveraging Spatial and Temporal Features for Accurate Cervical Cancer Diagnosis

Cervical cancer screening increasingly relies on automated image analysis to support colposcopic assessment and improve the consistency of clinical decision-making. This study proposes CerviDBF-Net, a novel deep learning framework for automated cervix-type classification from colposcopic images. The proposed architecture combines DenseNet121 for spatial feature extraction, a brain-inspired Liquid Neural Network (LNN) for contextual and temporal feature modelling, and a fully connected Softmax layer for final classification. To improve image quality and feature representation, the preprocessing pipeline incorporates bicubic interpolation, Bi-Histogram Equalization with Plateau Limit (BHEPL) for contrast enhancement, Kapur entropy-based thresholding, and the Roberts Cross operator for edge detection. The framework is evaluated on the Intel & MobileODT Cervical Cancer Screening dataset, which contains three cervix types (Type 1, Type 2, and Type 3) defined by the location and visibility of the transformation zone. CerviDBF-Net achieves an accuracy of 98.92%, sensitivity of 99.13%, specificity of 98.38%, precision of 98.69%, and an F1-score of 98.27%, demonstrating strong classification performance compared with the evaluated existing models. The proposed approach provides an automated baseline tool for cervix-type classification that may support triage in colposcopy-assisted screening by helping clinicians assess whether adequate visualization of the squamocolumnar junction has been achieved.