Conventional Neural Network-Based Automated Cervical Cancer Detection Technique

Cervical cancer continues to be a major global health concern, highlighting the need for early and precise detection to enhance patient outcomes. This study investigates the application of Convolutional Neural Networks (CNNs) for the automated detection of cervical cancer from medical images. We utilized a thorough dataset of Pap smear and colposcopy images, implementing robust preprocessing and data augmentation methods to improve model performance. Our CNN model attained an impressive accuracy of 94%, demonstrating strong precision, recall, and F1 scores for positive, negative, and suspected cases.A comparative analysis with baseline models and existing methods revealed the superiority of our approach, showcasing significant advancements in the field. The strong performance metrics emphasize the potential of deep learning techniques in medical diagnostics, especially in resource-limited settings where access to expert pathologists is limited. Although the study acknowledges limitations like dataset quality and computational resource demands, the findings highlight the transformative potential of incorporating CNNs into clinical workflows for early and accurate detection of cervical cancer. Future research will aim to broaden the dataset, enhance preprocessing techniques, and carry out real-world clinical validations to ensure the model’s practical applicability and reliability. This study marks a crucial advancement in improving cervical cancer screening and enhancing global health outcomes.