Cervical Cancer Detection Using a Hybrid CNN-Vision Transformer Model: A Comparative Study with Efficient NETB, DenseNET, Xception, And ResNET50
Year: 2025
Type: article
Abstract: Cervical cancer remains one of the leading causes of cancer-related deaths among women worldwide, particularly in low-resource settings. Early detection is crucial for improving survival rates, and advancements in deep learning have shown promise in automating this process. This paper proposes a novel hybrid model combining Convolutional Neural Networks (CNNs) with Vision Transformers (ViTs) for cervical cancer detection. We integrate EfficientNetB, DenseNet, Xception, and ResNet50 as backbone CNN architectures to extract hierarchical features, followed by a Vision Transformer to capture long-range dependencies and global context. The proposed model is evaluated on a publicly available cervical cancer dataset, achieving state-of-the-art accuracy, sensitivity, and specificity performance. Our results demonstrate the effectiveness of combining CNNs and ViTs for medical image analysis, providing a robust framework for cervical cancer detection.
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410076877