Identifying Cervical Cancer Lesions using Temporal Texture Analysis

D.I. Tapia López, A. Barreto Flores, and L. Altamirano Robles (Mexico)


Cervical cancer, temporal texture analysis, neural network, ROC analysis.


Cervical cancer is an important worldwide disease due to the high rate of incidence in the population. Several tests have been developed for detection of this illness; colposcopy is one of the diagnostic tests employed in recognition of lesions. Colposcopy performs a visual examination of the cervix based on temporal reaction of the surface stained with acetic acid, in order to identify lesions in cervix. In this paper we present an approach for identification of lesions based on temporal texture analysis in order to detect important patterns of lesions during colposcopy exam. Texture metrics based on spatial information are used in order to analyze temporally the acetic acid response and deduce appropriate signatures. Neural networks are used for classification process and results are presented using Receiver Operating Characteristic (ROC) analysis for visualizing classifier performance. Experimental results show a good accuracy in classification of lesions using temporal texture analysis.

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