{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T08:14:16Z","timestamp":1790928856589,"version":"4.1.0"},"reference-count":38,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T00:00:00Z","timestamp":1657584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Flemish University development aid","award":["SI-2020-82"],"award-info":[{"award-number":["SI-2020-82"]}]},{"name":"Research Direction of the University of Cuenca","award":["SI-2020-82"],"award-info":[{"award-number":["SI-2020-82"]}]},{"name":"Private Technical University of Loja","award":["SI-2020-82"],"award-info":[{"award-number":["SI-2020-82"]}]},{"name":"Vicerectorate of Reasearch of the University of Cuenca","award":["SI-2020-82"],"award-info":[{"award-number":["SI-2020-82"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>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 &amp; 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\u2019 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.<\/jats:p>","DOI":"10.3390\/diagnostics12071694","type":"journal-article","created":{"date-parts":[[2022,7,12]],"date-time":"2022-07-12T03:50:36Z","timestamp":1657597836000},"page":"1694","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Radiomics Diagnostic Tool Based on Deep Learning for Colposcopy Image Classification"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7155-5546","authenticated-orcid":false,"given":"Yuliana","family":"Jim\u00e9nez Gaona","sequence":"first","affiliation":[{"name":"Departamento de Qu\u00edmica y Ciencias Exactas, Universidad T\u00e9cnica Particular de Loja, San Cayetano Alto s\/n, Loja CP1101608, Ecuador"},{"name":"Instituto de Instrumentacion para la Imagen Molecular I3M, Universitat Polit\u00e9cnica de Valencia, E-46022 Valencia, Spain"},{"name":"Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L3G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1800-1189","authenticated-orcid":false,"given":"Darwin","family":"Castillo Malla","sequence":"additional","affiliation":[{"name":"Departamento de Qu\u00edmica y Ciencias Exactas, Universidad T\u00e9cnica Particular de Loja, San Cayetano Alto s\/n, Loja CP1101608, Ecuador"},{"name":"Instituto de Instrumentacion para la Imagen Molecular I3M, Universitat Polit\u00e9cnica de Valencia, E-46022 Valencia, Spain"},{"name":"Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L3G1, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2545-4733","authenticated-orcid":false,"given":"Bernardo","family":"Vega Crespo","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias M\u00e9dicas, Universidad de Cuenca, Cuenca 010203, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5829-9955","authenticated-orcid":false,"given":"Mar\u00eda Jos\u00e9","family":"Vicu\u00f1a","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias M\u00e9dicas, Universidad de Cuenca, Cuenca 010203, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1615-0746","authenticated-orcid":false,"given":"Vivian Alejandra","family":"Neira","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias M\u00e9dicas, Universidad de Cuenca, Cuenca 010203, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Santiago","family":"D\u00e1vila","sequence":"additional","affiliation":[{"name":"Facultad de Ciencias M\u00e9dicas, Universidad de Cuenca, Cuenca 010203, Ecuador"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3708-6501","authenticated-orcid":false,"given":"Veronique","family":"Verhoeven","sequence":"additional","affiliation":[{"name":"Family Medicine and Population Health, University of Antwerp, 2610 Antwerp, Belgium"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,7,12]]},"reference":[{"key":"ref_1","unstructured":"Bruni, L.B.R.L., Barrionuevo-Rosas, L., Albero, G., Serrano, B., Mena, M., Collado, J.J., Gomez, D., Munoz, J., Bosch, F.X., and de Sanjose, S. 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