Challenging the status quo: Why artificial intelligence models must go beyond accuracy in cervical cancer diagnosis

Cervical cancer is a significant health issue affecting women globally, with a high number of new cases and deaths reported each year. The disease is linked to HPV infection, but early detection through Pap smear tests can significantly increase performance. Deep learning techniques, particularly convolutional neural networks, transfer learning, generative adversarial networks, and attention mechanisms, are employed to identify cervical cancer. These innovative methods can increase the effectiveness and efficiency of cervical cancer screening and diagnosis. Although these technologies provide advantages for diagnosing cervical cancer, issues related to the availability and integrity of data, interpretability of models, and integration into clinical workflows exist. A computer-aided diagnostic system that uses vision transformers, a majority fusion mechanism, and explainable artificial intelligence is presented to address these challenges. This framework aims to increase cervical cancer detection accuracy and efficiency. Two cutting-edge datasets, DTU/Herlev and SIPaKMeD, are used to evaluate the system, yielding overall accuracy results of 99.22% and 99.8%, respectively. A comparison of the suggested framework with state-of-the-art methods revealed equivalent or even better results.