Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Kuriyodath, Ummu Salma; Inbanila; Jose, Iven Title A name given to the resource SwinTransConv and Tab-FCNN: a novel SwinTransConv neural network features and tab-fully connected neural network in pap smear images for cervical cancer classification Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Communications in Statistics: Simulation and Computation; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1080/03610918.2025.2584093" target="_blank" rel="noreferrer noopener">https://doi.org/10.1080/03610918.2025.2584093</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105026605740?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105026605740?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Kuriyodath U.S., Research Scholar, Department of Electronics &amp; Communication Engineering, Christ University, Bengaluru, India; Inbanila, Head and Associate Professor, Department of Electronics &amp; Communication Engineering, Christ University, Bengaluru, India; Jose I., Manipal Institute of Technology, Bengaluru, Manipal Academy of Higher Education, Manipal, India Description An account of the resource The primary objective of this paper is to delineate a Deep Learning (DL) methodology for cervical cancer from Pap smear imagery. In the interest of augmenting the quality and equilibrium of the dataset, the initial phase involved executing ROI detection on the Pap smear images. ROI detection is executed using YOLO V4 model in the input Pap smear images for detecting superficial, intermediate and parabasal layers. Then, the segmentation phase is performed to delinate cytoplasm and the nucleus, employing the YOLOv11 model. Subsequently, the feature extraction is executed by the proposed SwinTransConv, which integrates the Swin Transformer with a Convolutional Neural Network (CNN) to yield a robust and hierarchical representation of salient cellular features. The derived features act as input for the classification phase, for which a Tabular-Fully Convolutional Neural Network (Tab-FCNN) model is proposed by combining Tabular Network (TabNet) and Fully Convolutional Neural Networks (FCNN). TabNet identifies significant features from the input dataset utilizing attention-based mechanisms tailored for tabular data, whereas FCNN enhances the final decision-making process by assimilating complex feature interactions. Experimental findings state that the proposed approach reached an accuracy of 97.9%, a sensitivity of 95.4% and a specificity of 99.4%. 2026 Taylor &amp; Francis Group, LLC. Subject The topic of the resource Cervical cancer; Deep learning; Disease detection; Neural networks; Pap smear images Publisher An entity responsible for making the resource available Taylor and Francis Ltd. Relation A related resource ISSN: 3610918; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online