Leveraging swin transformer with ensemble of deep learning model for cervical cancer screening using colposcopy images

Cervical cancer (CC) is the leading cancer, which mainly affects women worldwide. It generally occurs from abnormal cell evolution in the cervix and a vital functional structure in the uterus. The importance of timely recognition cannot be overstated, which has led to various screening methods such as colposcopy, Human Papillomavirus (HPV) testing, and Pap smears to identify potential threats and enable early intervention. Early detection during the precancerous phase is crucial, as it provides an opportunity for effective treatment. The diagnosis and screening of CC depend on colposcopy and cytology models. Deep learning (DL) is an appropriate technique in computer vision, which has developed as a latent solution to increase the efficiency and accuracy of CC screening when equated to conventional clinical inspection models that are vulnerable to human error. This study presents a Leveraging Swin Transformer with an Ensemble of Deep Learning Model for Cervical Cancer Screening (LSTEDL-CCS) technique for colposcopy images. The presented LSTEDL-CCS technique aims to detect and classify CC on colposcopy images. Initially, the wiener filtering (WF) model is used for image pre-processing. Next, the swin transformer (ST) network is utilized for feature extraction. For the cancer detection process, the ensemble learning method is performed by employing three models, namely autoencoder (AE), bidirectional gated recurrent unit (BiGRU), and deep belief network (DBN). Finally, the hyperparameter tuning of the DL techniques is performed using the Pelican Optimization Algorithm (POA). A comprehensive experimental analysis is conducted, and the results are evaluated under diverse metrics. The performance validation of the LSTEDL-CCS methodology portrayed a superior accuracy value of 99.44% over existing models.