Recent advancement in cervical cancer diagnosis for automated screening: a detailed review

被引:24
作者
Chitra, B. [1 ]
Kumar, S. S. [2 ]
机构
[1] Noorul Islam Ctr Higher Educ, Dept Elect & Commun Engn, Kumaracoil, Tamil Nadu, India
[2] Noorul Islam Ctr Higher Educ, Dept Elect & Instrumentat Engn, Kumaracoil, Tamil Nadu, India
基金
英国科研创新办公室;
关键词
Cervical; Pap smear image; Soft computing; Publications; Cancer; IMAGE-ANALYSIS; CLASSIFICATION; INTEGRATION; ALGORITHM; WOMEN;
D O I
10.1007/s12652-021-02899-2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Cervical cancer is one of the most common and dangerous diseases for women. Initial diagnosis and classification of cervical cancer are to reduce the mortality rate. The Pap smear images are widely employed for the detection of cervical cancer in an automated manner; thereby, it enables reliable and accurate results. Recently, different kinds of soft computing techniques are used to deal with cervical cancer detection. In order to gain insight into recent advancements in the fields of study, this paper analyses most research papers between January 2010 and December 2020. This paper presents the graphical and organized view of the recent research works. The study explored the scope for further research in soft computing methods for the segmentation and classification of cervical cancer. The review also carried out an analysis of cervical cancer detection by categorizing the referred papers into techniques focused on soft computing. This study will provide information for researchers, publishers, and experts to examine emerging research patterns in the field of cervical cancer detection from pap smear images.
引用
收藏
页码:251 / 269
页数:19
相关论文
共 75 条
[41]  
Sanyal Parikshit, 2020, Med J Armed Forces India, V76, P418, DOI 10.1016/j.mjafi.2019.08.001
[42]  
Sarwar A., 2015, Personalized Medicine Universe, V4, P54, DOI 10.1016/j.pmu.2014.10.001
[43]   Towards rapid cervical cancer diagnosis: automated detection and classification of pathologic cells in phase-contrast images [J].
Schilling, T. ;
Miroslaw, L. ;
Glab, G. ;
Smereka, M. .
INTERNATIONAL JOURNAL OF GYNECOLOGICAL CANCER, 2007, 17 (01) :118-126
[44]   DCE-MRI pharmacokinetic parameter maps for cervical carcinoma prediction [J].
Shao, Jianbo ;
Zhang, Zhuo ;
Liu, Huiying ;
Song, Ying ;
Yan, Zhihan ;
Wang, Xue ;
Hou, Zujun .
COMPUTERS IN BIOLOGY AND MEDICINE, 2020, 118
[45]   Automated Image Analysis for High-Content Screening and Analysis [J].
Shariff, Aabid ;
Kangas, Joshua ;
Coelho, Luis Pedro ;
Quinn, Shannon ;
Murphy, Robert F. .
JOURNAL OF BIOMOLECULAR SCREENING, 2010, 15 (07) :726-734
[46]  
Sharma M., 2016, Indian Journal of Science and Technology, V9, P1, DOI [DOI 10.17485/ijst/2016/v9i28/98380, 10.17485/ijst/2016/v9i28/98380]
[47]  
Subashini, 2013, 3 INT C COMP SCI ENG
[48]   Improvement of Features Extraction Process and Classification of Cervical Cancer for the NeuralPap System [J].
Sulaiman, Siti Noraini ;
Mat-Isa, Nor Ashidi ;
Othman, Nor Hayati ;
Ahmad, Fadzil .
KNOWLEDGE-BASED AND INTELLIGENT INFORMATION & ENGINEERING SYSTEMS 19TH ANNUAL CONFERENCE, KES-2015, 2015, 60 :750-759
[49]   CCGPA-MPPT: Cauchy preferential crossover-based global pollination algorithm for MPPT in photovoltaic system [J].
Sundararaj, Vinu ;
Anoop, V ;
Dixit, Priyanka ;
Arjaria, Arundhati ;
Chourasia, Uday ;
Bhambri, Pankaj ;
Rejeesh, M. R. ;
Sundararaj, Regu .
PROGRESS IN PHOTOVOLTAICS, 2020, 28 (11) :1128-1145
[50]   Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction [J].
Sundararaj, Vinu .
INTERNATIONAL JOURNAL OF BIOMEDICAL ENGINEERING AND TECHNOLOGY, 2019, 31 (04) :325-345