Techniques for Images Processing, Factors and Results of Colposcopy to Diagnose Cervical Cancer
Alfonso Alexander Ruesta Sedano1, Jeanette Giuliana Gamarra Herrera2, Lenis Rossi Wong Portillo3
1Alfonso A. Ruesta Sedano, National University of San Marcos, Peru, South America.
2Jeanette G. Gamarra Herrera, National University of San Marcos, Peru South America.
3Lenis R. Wong Portillo, National University of San Marcos, Peru, South America.
Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 128-133 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.C4284099320 | DOI: 10.35940/ijrte.C4284.099320
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: The colposcopy is a test that is performed if you have relationed symptoms with cancer or if the result of Pap smears test gives an abnormal cells; however, it has a continue problem because there are few doctors who know about colposcopy and it leads to misinterpretation. Therefore, in the last years various proposals have emerged to solve this problem. The present study aims to identify the current state of the latest research related to the detection of cervical cancer during the colposcopy test using the image evaluation. A framework is proposed based on 3 research questions: (1) What techniques are used for image processing with colposcopy to diagnose cervical cancer? (2) What are the factors that help diagnose cervical cancer during colposcopy? And (3) What results corroborate or provide the diagnosis produced by the colposcopy test in the detection of cervical cancer? One of the results proposes that the use of Convolution Neural Network (CNN) improves the sensitivity of the diagnosis of cervical cancer, since it achieved greater precision in colposcopy image processing. Furthermore, the diagnosis can be corroborated with the “results” of the “Biopsy” and “Expert Judgment”.
Keywords: Colposcopy, Colposcopy techniques, Colposcopy image, Convolutional Neuronal Network.