Improved Integrated Deep Model for Pap-Smear Cell Analysis  

作　　者：Somasundaram DEVARAJ Nirmala MADIAN Gnanasaravanan SUBRAMANIAM Rithaniya CHELLAMUTHU Muralitharan KRISHANAN 

机构地区：[1]School of Electronics Engineering,Vellore Institute of Technology,Vellore 632014,India [2]Department of Biomedical Engineering,Dr.N.G.P Institute of Technology,Coimbatore 641045,India [3]Karunya Institute of Technology and Sciences,Coimbatore 641114,India [4]Department of Computer Science,Institute of Mathematical Science,Sungkyunkwan University,Suwon 16419,South Korea

出　　处：《Journal of Systems Science and Information》2024年第1期113-124,共12页系统科学与信息学报（英文）

摘　　要：Cervical cancer is the fourth most common malignancy to strike a woman globally.If discovered early enough,it can be effectively treated.Although there is a chance of error owing to human error,the Pap smear is a good tool for first screening for cervical cancer.It also takes a lot of time and effort to complete.The aim of this study was to reduce the possibility of error by automating the process of classifying cervical cancer using Pap smear images.For the purpose of this study,dual convolution neural networks with LSTM were employed to classify images due to deep learning approaches inspire distinct features and powerful classifiers for many computer vision applications.The proposed deep learning model based on convolution neural networks(CNN)with the long short-term memory(LSTM)network is to learn features which give better recognition accuracy.The overall model is known as Smear-net.In which‘smear’indicates‘pap-smear cancer cells’and‘net’refers to neural network.The parameters such as,Accuracy,Precision,Recall,Accuracy,Sensitivity,and Specificity are used to validate the models.The proposed method provides the improved accuracy of 99.57 percentage for classification of the pap-smear cells.The proposed approaches demonstrate the effectiveness of our contributions by testing and comparing with the state-of-the-art techniques.

关 键 词：pap-smear deep learning convolution neural network smear-net 

分 类 号：R737.33[医药卫生—肿瘤] TP183[医药卫生—临床医学] TP391.41[自动化与计算机技术—控制理论与控制工程]