Multi-class nucleus detection and classification using deep convolutional neural network with enhanced high dimensional dissimilarity translation model on cervical cells

摘要

Advanced cervical screening via liquid-based cytology (LBC)/Pap smear is a highly efficient precancerous cell detection tool based on cell image analysis, in which cells are classified as normal/abnormal. This paper outlines the drawbacks by introducing a new framework for the accurate classification of cervical cells. The proposed methodology comprises three phases: segmentation, localization of nucleus, and classification. In the segmentation phase, we develop a hybrid system that incorporates two binary image patches obtained by a 19-layered convolutional neural network (ConvNet) model with an enhanced deep high dimensional dissimilarity translation (HDDT) based conspicuous segmentation. To get the relevant information from binary patched images, a technique called optimum semantic similarity selective search (OSS-SS) is proposed that returns the localized RGB patched image. A pre-trained ResNet-50 model is retrained using transfer learning on localized patched images in the classification phase. Following that, the selected features from the average pool and fully connected layers are down-sampled using the t-distribution stochastic neighbor embedding (t-SNE) approach. Finally, these combined features are fed into a multi-class weighted kernel extreme learning machine (WKELM) classifier via a sparse multicanonical correlation (SMCCA) method. Three datasets (SIPaKMed, CRIC, and Harlev) are used to evaluate the segmentation and classification task. The proposed approach obtained an accuracy of 99.12 , specificity of 99.45 , sensitivity of 99.25 with an execution time 99.6248 on SIPaKMed. The experimental analysis indicate that our model is more effective than existing techniques. (c) 2022 Nalecz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences. Published by Elsevier B.V.