Cervix Visionator ELM: A Novel Approach to Early Detection of Cervical Cancer

Cervical cancer is one of the leading causes of mortality among women, but early detection and treatment can prevent its progression. Although Pap smear images are a prevalent method of cervical cancer screening, manual diagnosis is laborious and prone to error. The Cervix Visionator ELM, a novel automated computerized approach for detecting cervical cancer in Pap smear images, is introduced in this research article. We integrate the self-attention mechanism with EfficientNet, a state-of-the-art Convolutional Neural Network (CNN) architecture, and Vision Transformer (ViT) models to get deep-learned features from Pap smear images. The extracted features are then classified using an Extreme Learning Machine (ELM)-based classifier. Our model was assessed using the SIPaKMeD open dataset. In cervical cells, the Cervix Visionator ELM demonstrated an accuracy of 98.89%, 99.42% precision, 97.87% recall, and 98.76% F-measure. The findings of this study provide evidence for the soundness and efficacy of our model, establishing its superiority over the majority of current models utilized for classifying cervical cells.