Diagnosis of Cervical Cancer based on Ensemble Deep Learning Network using Colposcopy Images

被引:115
作者
Chandran, Venkatesan [1 ]
Sumithra, M. G. [1 ]
Karthick, Alagar [2 ]
George, Tony [3 ]
Deivakani, M. [4 ]
Elakkiya, Balan [5 ]
Subramaniam, Umashankar [6 ]
Manoharan, S. [7 ]
机构
[1] KPR Inst Engn & Technol, Dept Elect & Commun Engn, Avinashi Rd, Coimbatore 641407, Tamil Nadu, India
[2] KPR Inst Engn & Technol, Dept Elect & Elect Engn, Renewable Energy Lab, Avinashi Rd, Coimbatore 641407, Tamil Nadu, India
[3] Adi Shankara Inst Engn & Technol Mattoor, Dept Elect & Elect Engn, Kalady 683574, Kerala, India
[4] PSNA Coll Engn & Technol, Dept Elect & Commun Engn, Dindigul 624622, Tamil Nadu, India
[5] Vel Tech High Tech Dr Rangarajan Dr Sakunthala En, Dept Elect & Commun Engn, Chennai 600062, Tamil Nadu, India
[6] Prince Sultan Univ, Coll Engn, Renewable Energy Lab, Dept Commun & Networks, Riyadh 12435, Saudi Arabia
[7] Ambo Univ, Inst Technol, Sch Informat & Elect Engn, Dept Comp Sci, Post Box 19, Ambo, Ethiopia
基金
英国科研创新办公室;
关键词
CELL CLASSIFICATION; PREDICTION; GUIDELINES; MODEL;
D O I
10.1155/2021/5584004
中图分类号
Q81 [生物工程学(生物技术)]; Q93 [微生物学];
学科分类号
071005 ; 0836 ; 090102 ; 100705 ;
摘要
Traditional screening of cervical cancer type classification majorly depends on the pathologist's experience, which also has less accuracy. Colposcopy is a critical component of cervical cancer prevention. In conjunction with precancer screening and treatment, colposcopy has played an essential role in lowering the incidence and mortality from cervical cancer over the last 50 years. However, due to the increase in workload, vision screening causes misdiagnosis and low diagnostic efficiency. Medical image processing using the convolutional neural network (CNN) model shows its superiority for the classification of cervical cancer type in the field of deep learning. This paper proposes two deep learning CNN architectures to detect cervical cancer using the colposcopy images; one is the VGG19 (TL) model, and the other is CYENET. In the CNN architecture, VGG19 is adopted as a transfer learning for the studies. A new model is developed and termed as the Colposcopy Ensemble Network (CYENET) to classify cervical cancers from colposcopy images automatically. The accuracy, specificity, and sensitivity are estimated for the developed model. The classification accuracy for VGG19 was 73.3%. Relatively satisfied results are obtained for VGG19 (TL). From the kappa score of the VGG19 model, we can interpret that it comes under the category of moderate classification. The experimental results show that the proposed CYENET exhibited high sensitivity, specificity, and kappa scores of 92.4%, 96.2%, and 88%, respectively. The classification accuracy of the CYENET model is improved as 92.3%, which is 19% higher than the VGG19 (TL) model.
引用
收藏
页数:15
相关论文
共 45 条
[1]   Classification and diagnosis of cervical cancer with softmax classification with stacked autoencoder [J].
Adem, Kemal ;
Kilicarslan, Serhat ;
Comert, Onur .
EXPERT SYSTEMS WITH APPLICATIONS, 2019, 115 :557-564
[2]   Detection of myocardial infarction based on novel deep transfer learning methods for urban healthcare in smart cities [J].
Alghamdi, Ahmed ;
Hammad, Mohamed ;
Ugail, Hassan ;
Abdel-Raheem, Asmaa ;
Muhammad, Khan ;
Khalifa, Hany S. ;
Abd El-Latif, Ahmed A. .
MULTIMEDIA TOOLS AND APPLICATIONS, 2024, 83 (05) :14913-14934
[3]   State of Charge Estimation of Lithium-Ion Battery for Electric Vehicles Using Machine Learning Algorithms [J].
Chandran, Venkatesan ;
Patil, Chandrashekhar K. ;
Karthick, Alagar ;
Ganeshaperumal, Dharmaraj ;
Rahim, Robbi ;
Ghosh, Aritra .
WORLD ELECTRIC VEHICLE JOURNAL, 2021, 12 (01)
[4]   Classification of cervical neoplasms on colposcopic photography using deep learning [J].
Cho, Bum-Joo ;
Choi, Youn Jin ;
Lee, Myung-Je ;
Kim, Ju Han ;
Son, Ga-Hyun ;
Park, Sung-Ho ;
Kim, Hong-Bae ;
Joo, Yeon-Ji ;
Cho, Hye-Yon ;
Kyung, Min Sun ;
Park, Young-Han ;
Kang, Byung Soo ;
Hur, Soo Young ;
Lee, Sanha ;
Park, Sung Taek .
SCIENTIFIC REPORTS, 2020, 10 (01)
[5]   An intensity factorized thresholding based segmentation technique with gradient discrete wavelet fusion for diagnosing stroke and tumor in brain MRI [J].
Deepa, B. ;
Sumithra, M. G. .
MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING, 2019, 30 (04) :2081-2112
[6]   Inception v3 based cervical cell classification combined with artificially extracted features [J].
Dong, N. ;
Zhao, L. ;
Wu, C. H. ;
Chang, J. F. .
APPLIED SOFT COMPUTING, 2020, 93
[7]   Cervical cancer classification using convolutional neural networks and extreme learning machines [J].
Ghoneim, Ahmed ;
Muhammad, Ghulam ;
Hossain, M. Shamim .
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2020, 102 :643-649
[8]   MLSeq: Machine learning interface for RNA-sequencing data [J].
Goksuluk, Dincer ;
Zararsiz, Gokmen ;
Korkmaz, Selcuk ;
Eldem, Vahap ;
Zararsiz, Gozde Erturk ;
Ozcetin, Erdener ;
Ozturk, Ahmet ;
Karaagaoglu, Ahmet Ergun .
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 2019, 175 :223-231
[9]   Bayesian multitask learning regression for heterogeneous patient cohorts [J].
Goncalves A. ;
Ray P. ;
Soper B. ;
Widemann D. ;
Nygård M. ;
Nygård J.F. ;
Sales A.P. .
Journal of Biomedical Informatics: X, 2019, 4
[10]   A Multitier Deep Learning Model for Arrhythmia Detection [J].
Hammad, Mohamed ;
Iliyasu, Abdullah M. ;
Subasi, Abdulhamit ;
Ho, Edmond S. L. ;
Abd El-Latif, Ahmed A. .
IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT, 2021, 70 (70)