Enhancing Cervical Pre-Cancerous Classification Using Advanced Vision Transformer.

One of the most common types of cancer among in women is cervical cancer. Incidence and fatality rates are steadily rising, particularly in developing nations, due to a lack of screening facilities, experienced specialists, and public awareness. Visual inspection is used to screen for cervical cancer after the application of acetic acid (VIA), histopathology test, Papanicolaou (Pap) test, and human papillomavirus (HPV) test. The goal of this research is to employ a vision transformer (ViT) enhanced with shifted patch tokenization (SPT) techniques to create an integrated and robust system for automatic cervix-type identification. A vision transformer enhanced with shifted patch tokenization is used in this work to learn the distinct features between the three different cervical pre-cancerous types. The model was trained and tested on 8215 colposcopy images of the three types, obtained from the publicly available mobile-ODT dataset. The model was tested on 30% of the whole dataset and it showed a good generalization capability of 91% accuracy. The state-of-the art comparison indicated the outperformance of our model. The experimental results show that the suggested system can be employed as a decision support tool in the detection of the cervical pre-cancer transformation zone, particularly in low-resource settings with limited experience and resources.