The Prediction of Cervical Cancer using Resnet and VGG16 (Hybrid Resnet)
Keywords:
Convolutional Neural Network (CNN), Deep learning, Feature extraction, Medical image classification, Transfer learningAbstract
Cervical cancer remains one of the leading causes of death among women worldwide, particularly in low-resource settings. Early prediction significantly increases the chances of successful treatment and survival. In this study, we propose a deep learning-based approach for the automated prediction of cervical cancer using Convolutional Neural Networks (CNN) and ResNet, a powerful pre-trained model. The dataset comprises cervical cell images that are preprocessed and augmented to improve training efficiency and accuracy. The ResNet model is fine-tuned and compared with a custom-built CNN to evaluate performance in terms of accuracy, sensitivity, specificity, and F1-score. Our results demonstrate that ResNet outperforms traditional CNNs in terms of feature extraction and classification accuracy, making it a promising tool for early-stage cervical cancer diagnosis. This research highlights the potential of deep learning models to support clinical decision-making and improve diagnostic capabilities in healthcare systems.
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