Cervical Cancer Diagnosis Based on Multi-Domain Features Using Deep Learning Enhanced by Handcrafted Descriptors

Cervical cancer, among the most frequent adverse cancers in women, could be avoided through routine checks. The Pap smear check is a widespread screening methodology for timely identification of cervical but it susceptible to human mistakes. Artificial Intelligence-reliant computer-aided diagnostic (CAD) methods have been extensively explored identify cancer order enhance conventional testing procedure. In attain remarkable classification results, current CAD systems require pre-segmentation steps extraction cells from pap slide, which complicated task. Furthermore, some models use only hand-crafted feature cannot guarantee sufficiency phases. addition, if there are few data samples, such as cell datasets, deep learning (DL) alone not perfect choice. existing obtain attributes one domain, integration features multiple domains usually increases performance. Hence, this article presents model based on extracting domain. It does process thus less complex than methods. employs three compact DL high-level spatial rather utilizing an individual with large number parameters and layers used CADs. Moreover, retrieves several statistical textural descriptors including time–frequency instead employing single domain demonstrate clearer representation features, case examines influence each set handcrafted accuracy independently hybrid. then consequences combining obtained CNN combined features. Finally, uses principal component analysis merge entire investigate effect merging numerous various results. With 35 components, achieved by quatric SVM proposed reached 100%. performance described proves that able boost accuracy. Additionally, comparative analysis, along other present studies, shows competing capacity CAD.