Deep Learning based Cervical Cancer Classification and Segmentation from Pap Smears Images using an EfficientNet

Abstract

One of the most prevalent cancers in the world, cervical cancer claims the lives of many people every year. Since early cancer diagnosis makes it easier for patients to use clinical applications, cancer research is crucial. The Pap smear is a useful tool for early cervical cancer detection, although the human error is always a risk. Additionally, the procedure is laborious and time-consuming. By automatically classifying cervical cancer from Pap smear images, the study's goal was to reduce the risk of misdiagnosis. For picture enhancement in this study, contrast local adaptive histogram equalization (CLAHE) was employed. Then, from this cervical image, features including wavelet, morphological features, and Grey Level Co-occurrence Matrix (GLCM) are extracted. An effective network trains and tests these derived features to distinguish between normal and abnormal cervical images by using EfficientNet. On the aberrant cervical picture, the SegNet method is used to identify and segment the cancer zone. Specificity, accuracy, positive predictive value, Sensitivity, and negative predictive value are all utilized to analyze the suggested cervical cancer detection system performances. When used on the Herlev benchmark Pap smear dataset, results demonstrate that the approach performs better than many of the existing algorithms.