Development of Algorithms for Automated Detection of Cervical Pre-Cancers With a Low-Cost, Point-of-Care, Pocket Colposcope.
IEEE Trans Biomed Eng
; 66(8): 2306-2318, 2019 08.
Article
em En
| MEDLINE
| ID: mdl-30575526
ABSTRACT
GOAL In this paper, we propose methods for (1) automatic feature extraction and classification for acetic acid and Lugol's iodine cervigrams and (2) methods for combining features/diagnosis of different contrasts in cervigrams for improved performance. METHODS:
We developed algorithms to pre-process pathology-labeled cervigrams and extract simple but powerful color and textural-based features. The features were used to train a support vector machine model to classify cervigrams based on corresponding pathology for visual inspection with acetic acid, visual inspection with Lugol's iodine, and a combination of the two contrasts.RESULTS:
The proposed framework achieved a sensitivity, specificity, and accuracy of 81.3%, 78.6%, and 80.0%, respectively, when used to distinguish cervical intraepithelial neoplasia (CIN+) relative to normal and benign tissues. This is superior to the average values achieved by three expert physicians on the same data set for discriminating normal/benign cases from CIN+ (77% sensitivity, 51% specificity, and 63% accuracy).CONCLUSION:
The results suggest that utilizing simple color- and textural-based features from visual inspection with acetic acid and visual inspection with Lugol's iodine images may provide unbiased automation of cervigrams.SIGNIFICANCE:
This would enable automated, expert-level diagnosis of cervical pre-cancer at the point of care.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Lesões Pré-Cancerosas
/
Algoritmos
/
Interpretação de Imagem Assistida por Computador
/
Neoplasias do Colo do Útero
/
Colposcópios
Tipo de estudo:
Diagnostic_studies
/
Health_economic_evaluation
/
Screening_studies
Limite:
Female
/
Humans
Idioma:
En
Ano de publicação:
2019
Tipo de documento:
Article