Aceto-white temporal pattern classification using k-NN to identify precancerous cervical lesion in colposcopic images
| Autor: | Nicandro Cruz-Ramírez, Rodolfo Hernández-Jiménez, Héctor-Gabriel Acosta-Mesa |
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| Rok vydání: | 2009 |
| Předmět: |
Adult
Uterine Cervical Neoplasms Cervical lesion Pilot Projects Health Informatics Young Adult Artificial Intelligence Image Interpretation Computer-Assisted medicine Humans Computer Simulation Diagnosis Computer-Assisted Mexico Cervical cancer Colposcopy medicine.diagnostic_test business.industry Pattern recognition medicine.disease Computer Science Applications Cervical tissue Color changes Female Artificial intelligence business Precancerous Conditions Algorithms |
| Zdroj: | Computers in Biology and Medicine. 39:778-784 |
| ISSN: | 0010-4825 |
| DOI: | 10.1016/j.compbiomed.2009.06.006 |
| Popis: | After Pap smear test, colposcopy is the most used technique to diagnose cervical cancer due to its higher sensitivity and specificity. One of the most promising approaches to improve the colposcopic test is the use of the aceto-white temporal patterns intrinsic to the color changes in digital images. However, there is not a complete understanding of how to use them to segment colposcopic images. In this work, we used the classification algorithm k-NN over the entire length of the aceto-white temporal pattern to automatically discriminate between normal and abnormal cervical tissue, reaching a sensitivity of 71% and specificity of 59%. |
| Databáze: | OpenAIRE |
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