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DC Field | Value | Language |
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dc.contributor.author | Vicuña, María José | |
dc.contributor.author | Jiménez Gaona, Yuliana | |
dc.contributor.author | Verhoeven, Veronique M. | |
dc.contributor.author | Castillo Malla, Darwin Patricio | |
dc.contributor.author | Davila Sacoto, Santiago Arturo | |
dc.contributor.author | Vega Crespo, Bernardo Jose | |
dc.contributor.author | Neira Molina, Viviana Alejandra | |
dc.date.accessioned | 2023-01-23T20:16:16Z | - |
dc.date.available | 2023-01-23T20:16:16Z | - |
dc.date.issued | 2022 | |
dc.identifier.issn | 2075-4418 | |
dc.identifier.uri | http://dspace.ucuenca.edu.ec/handle/123456789/40832 | - |
dc.identifier.uri | https://www.scopus.com/record/display.uri?eid=2-s2.0-85137351772&doi=10.3390%2fdiagnostics12071694&origin=inward&txGid=019aacd41c1d9887ad3ede82fdf02a41 | |
dc.description.abstract | Background: Colposcopy imaging is widely used to diagnose, treat and follow-up on premalignant and malignant lesions in the vulva, vagina, and cervix. Thus, deep learning algorithms are being used widely in cervical cancer diagnosis tools. In this study, we developed and preliminarily validated a model based on the Unet network plus SVM to classify cervical lesions on colposcopy images. Methodology: Two sets of images were used: the Intel & Mobile ODT Cervical Cancer Screening public dataset, and a private dataset from a public hospital in Ecuador during a routine colposcopy, after the application of acetic acid and lugol. For the latter, the corresponding clinical information was collected, specifically cytology on the PAP smear and the screening of human papillomavirus testing, prior to colposcopy. The lesions of the cervix or regions of interest were segmented and classified by the Unet and the SVM model, respectively. Results: The CAD system was evaluated for the ability to predict the risk of cervical cancer. The lesion segmentation metric results indicate a DICE of 50%, a precision of 65%, and an accuracy of 80%. The classification results’ sensitivity, specificity, and accuracy were 70%, 48.8%, and 58%, respectively. Randomly, 20 images were selected and sent to 13 expert colposcopists for a statistical comparison between visual evaluation experts and the CAD tool (p-value of 0.597). Conclusion: The CAD system needs to improve but could be acceptable in an environment where women have limited access to clinicians for the diagnosis, follow-up, and treatment of cervical cancer; better performance is possible through the exploration of other deep learning methods with larger datasets. | |
dc.language.iso | es_ES | |
dc.source | Diagnostics | |
dc.subject | Deep learning | |
dc.subject | Unet | |
dc.subject | Cervical coloscopy | |
dc.subject | Lesion classification | |
dc.title | Radiomics Diagnostic Tool Based on Deep Learning for Colposcopy Image Classification | |
dc.type | ARTÍCULO | |
dc.ucuenca.idautor | 0104947700 | |
dc.ucuenca.idautor | 0000-0001-7155-5546 | |
dc.ucuenca.idautor | 0000-0002-1800-1189 | |
dc.ucuenca.idautor | 0000-0002-3708-6501 | |
dc.ucuenca.idautor | 0301630802 | |
dc.ucuenca.idautor | 0102146917 | |
dc.ucuenca.idautor | 0000-0001-5829-9955 | |
dc.identifier.doi | 10.3390/diagnostics12071694 | |
dc.ucuenca.version | Versión publicada | |
dc.ucuenca.areaconocimientounescoamplio | 09 - Salud y Bienestar | |
dc.ucuenca.afiliacion | Jiménez, Y., Universidad Técnica Particular de Loja, Loja, Ecuador; Jiménez, Y., Universitat Politècnica de València, Valencia, España; Jiménez, Y., University of Waterloo, Waterloo, Canada | |
dc.ucuenca.afiliacion | Castillo, D., Universidad Técnica Particular de Loja, Loja, Ecuador; Castillo, D., Universidad Politécnica de Madrid, Madrid, España; Castillo, D., University of Waterloo, Waterloo, Canada | |
dc.ucuenca.afiliacion | Vega, B., Universidad de Cuenca, Facultad de Ciencias Médicas, Cuenca, Ecuador | |
dc.ucuenca.afiliacion | Davila, S., Universidad de Cuenca, Facultad de Ciencias Médicas, Cuenca, Ecuador | |
dc.ucuenca.afiliacion | Vicuña, M., Universidad de Cuenca, Facultad de Ciencias Médicas, Cuenca, Ecuador | |
dc.ucuenca.afiliacion | Neira, V., Universidad de Cuenca, Facultad de Ciencias Médicas, Cuenca, Ecuador | |
dc.ucuenca.afiliacion | Verhoeven, V., Universidad de Amberes (University of Antwerp), Amberes, Belgica | |
dc.ucuenca.correspondencia | Vega Crespo, Bernardo Jose, bernardo.vegac@ucuenca.edu.ec | |
dc.ucuenca.volumen | Volumen 12, número 7 | |
dc.ucuenca.indicebibliografico | SCOPUS | |
dc.ucuenca.factorimpacto | 0.658 | |
dc.ucuenca.cuartil | Q2 | |
dc.ucuenca.numerocitaciones | 0 | |
dc.ucuenca.areaconocimientofrascatiamplio | 3. Ciencias Médicas y de la Salud | |
dc.ucuenca.areaconocimientofrascatiespecifico | 3.2 Medicina Clínica | |
dc.ucuenca.areaconocimientofrascatidetallado | 3.2.2 Ginecología y Obstetricia | |
dc.ucuenca.areaconocimientounescoespecifico | 091 - Salud | |
dc.ucuenca.areaconocimientounescodetallado | 0914 - Tecnologías de Diagnóstico y Tratamiento Médico | |
dc.ucuenca.urifuente | https://www.mdpi.com/ | |
Appears in Collections: | Artículos |
Files in This Item:
File | Size | Format | |
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documento.pdf | 3.54 MB | Adobe PDF | View/Open |
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