Deep learning-based decision support system for cervical cancer identification in liquid-based cytology pap smears - Atteia, Ghada; Alabdulhafith, Maali; Abdallah, Hanaa A; Abdel Samee, Nagwan; Alayed, Walaa (Technology and health care, 2025)
BackgroundCervical cancer is the fourth most common cause of women cancer deaths worldwide. The primary etiology of cervical cancer is the persistent infection of specific high-risk strains of the human papillomavirus. Liquid-based cytology is the established method for early detection of cervical cancer. The evaluation of cellular abnormalities at a microscopic level allows for the identification of malignant or precancerous features in liquid-based cytology pap smears. This technique is characterized by its time-consuming nature and susceptibility to both inter- and intra-observer variability. Hence, the utilization of Artificial Intelligence in computer-assisted diagnosis can reduce the duration needed for diagnosing this ailment, thereby eliminating delayed diagnosis and facilitating the implementation of an efficient treatment.ObjectiveThis research presents a new deep learning-based cervical cancer identification decision support system in liquid-based cytology smear images.MethodsThe proposed diagnosis support system incorporates a novel hybrid feature reduction and optimization module, which integrates a sparse Autoencoder with the Binary Harris Hawk metaheuristic optimization algorithm to select the most informative features from a supplemented feature set of the input images. The supplemented feature set is retrieved by three pretrained Convolutional Neural Networks. The module utilizes an improved feature set to conduct a Bayesian-optimized K Nearest Neighbors machine learning classification of cervical cancer in input Pap smears.ResultsThe introduced approach achieves a classification accuracy of 99.9% and demonstrates an improved ability to detect the stages of cervical cancer, with a sensitivity of 99.8%. In addition, the system has the ability to identify the lack of cervical cancer stages with a specificity rate of 99.9%.ConclusionThe proposed system outpaces recent deep learning-based cervical cancer identification systems.