Performance Analysis of Five U-Nets on Cervical Cancer Datasets

Abstract

Image segmentation is crucial for precise analysis and classification of biomedical images, especially in the realm of cervical cancer detection. The accuracy of segmentation significantly influences the efficacy of subsequent image classification processes. While traditional algorithms exist for image segmentation, recent advancements in convolutional neural networks, particularly U-Nets have showcased exceptional effectiveness, especially in the realm of biomedical imaging. This research focuses on evaluating the accuracy of various U-Net architectures applied to three distinct cervical cancer datasets i.e., DSB containing 1340 images, SipakMed containing 1849 images and Intel Images for Screening containing 2000 images datasets taken from 2018 Data Science Bowl. The investigated U-Net architectures comprise the fundamental U-Net, Attention U-Net, Double U-Net, Spatial Attention U-Net, and Residual U-Net. The performance of the u-nets is judged on the metrics: Recall, Precision, F1, Jaccard and Accuracy. It is observed that Basic U-Net on the DSB dataset provides highest value on these metrices and accuracy obtained is 96%. The reason of high accuracy for DSB dataset can be attributed to the contrast of the images which by using co-occurrence matrix is calculated as 145.