Abstract
Cervical cancer remains a critical global health issue, necessitating more accurate diagnostic techniques for effective management. Traditional methods, which rely heavily on human analysis of cervicography images, are hampered by significant limitations such as variability in interpretation and a shortage of specialists, especially in low- and middle-income countries. This study introduces an advanced approach using a convolutional neural network (CNN), specifically the DenseNet121 architecture, to enhance the classification accuracy of cervical intraepithelial neoplasia (CIN) and normal cervix cases. We employed robust k-fold cross-validation on images sourced from the International Agency for Research on Cancer (IARC) to train and refine our model. Subsequent testing on a separate dataset from "Hospital Zambrano Hellion, Tec Salud" allowed us to evaluate the model’s effectiveness in a real-world clinical setting. The results indicate promising improvements in diagnostic accuracy, suggesting that the CNN-based approach could significantly enhance the current methods used for cervical cancer screening and diagnosis.