CerviNet: A Novel Approach for Cervical Cancer Classification Using Pap-Smear Images

Abstract

Cervical cancer is the fourth most common disease among women worldwide, and pap smear images are used as a primary diagnostic technique to detect precancerous and cancerous abnormalities in the cervix, vagina, and vulva. Deep learning algorithms have gained popularity in developing automated computer-aided diagnostic systems to solve the difficulties associated with manual assessment. This article introduces an innovative hybrid approach to effectively and accurately categorizing cervical cells. The proposed model employs advanced data enhancement techniques, including resampling to address class imbalance and augmentation (e.g., random horizontal flips and rotations) to increase dataset diversity and improve generalization. These strategies help the model handle different types of data more effectively, making it more adaptable and reliable in real-world scenarios. We use Vision Transformer’s (ViT) linear projection and position embedding to change the input images into patches that can be sent to a transformer encoder. A fusion architecture is established by incorporating supplementary convolutional layers, followed by a fully connected layer, to improve the features extracted by the model. The ViT-based model is developed using pretrained weights and allows fine-tuning to address problems with cervical cancer classification efficiently. To enhance the quality of these cell images, we employ median smoothing and Gaussian filtering as preprocessing techniques. The experiment results demonstrate the proposed methodology’s potential for improving the precision of cervical cancer classification. Notably, our model exhibited outstanding accuracy on the 2-state classification on the Herlev dataset and the 3-state classification on the SIPaKMeD dataset, at 98.07% and 98.08%, respectively. The model’s ability to effectively categorize cervical cancer images across various datasets is evidenced by the accuracy rates specific to each dataset. This indicates the model’s robustness and promise for practical clinical use.