CNN-based Method for Classifying Cervical Cancer Cells in Pap Smear Images

Background: The absence of reliable early treatment serves as one of the main causes of cervical cancer. Hence, it is crucial to detect cervical cancer early. The biggest challenge in diagnosing cervical cancer is that it is asymptomatic until it develops into invasive carcinoma. In medical applications, the use of machine learning and deep learning is successful as a classifier in the preliminary identification of cancerous cells in the cervical region. Methods: In our study, we present a CNN-based method for the classification of cervical cancer cells. We present a method for accurately classifying Pap smear images into abnormal or healthy cells by extracting essential information using a variety of deep-learning approaches. Experiments are performed using the SIPaKMeD and Herlev datasets. Several pre-trained convolutional neural network (CNN) models are used via transfer learning methods, hence predicting and evaluating the accurate classifier with the best optimal solution. Classification of cervical cell clusters in whole slide images (WSI) has usually comprised two stages: segmentation to extract individual cell patches, and subsequently single-cell categorization. Results: As a result, segmentation accuracy determines the classification pipeline's performance. We propose a direct classification of WSI cervical cell groups without segmentation and demonstrate that segmentation is not strictly necessary for good classification results. Conclusion: Our solution outperformed prior methods and benchmarks, with an accuracy of 96.74% for WSI patches and 97.55% for full-cell images for the SIPaKMeD dataset, and an accuracy of 90.42% for the Herlev dataset. The results show that the suggested approach may accurately distinguish cervical cancerous and non-cancerous cells.