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

• Published in: BioMed research international Vol. 2021; no. 1; p. 5584004
• Main Authors: Chandran, Venkatesan, Sumithra, M. G., Karthick, Alagar, George, Tony, Deivakani, M., Elakkiya, Balan, Subramaniam, Umashankar, Manoharan, S.
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2021; John Wiley & Sons, Inc
• Subjects: Artificial neural networks; Cancer; Cervical cancer; Cervix; Classification; Colposcopy; Critical components; Deep learning; Diagnosis; Health aspects; Human papillomavirus; Identification; Image classification; Image processing; Information processing; Machine learning; Medical imaging; Medical screening; Methods; Model accuracy; Neural networks; Pap smear; Patient outcomes; Sensitivity; Support vector machines; Transfer learning; Womens health; India
• ISSN: 2314-6133; 2314-6141; 2314-6141
• DOI: 10.1155/2021/5584004

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.