Classification of Images Acquired with Colposcopy Using Artificial Neural Networks. (January 2014)

• Record Type: Journal Article
• Authors: Simões, Priscyla W.; Izumi, Narjara B.; Casagrande, Ramon S.; Venson, Ramon; Veronezi, Carlos D.; Moretti, Gustavo P.; da Rocha, Edroaldo L.; Cechinel, Cristian; Ceretta, Luciane B.; Comunello, Eros; Martins, Paulo J.; Casagrande, Rogério A.; Snoeyer, Maria L.; Manenti, Sandra A.
• Is Part Of: Cancer informatics. Volume 13(2014)
• Publication Date: 2014-01
• Subjects: colposcopy -- artificial intelligence -- neural networks -- medical informatics -- computer-assisted diagnosis; Bioinformatics -- Periodicals; Biology -- Data processing -- Periodicals; Cancer -- Periodicals; Cancer -- Research -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.4137/CIN.S17948
• Languages: English
• ISSNs: 1176-9351

Abstract

Objective: To explore the advantages of using artificial neural networks (ANNs) to recognize patterns in colposcopy to classify images in colposcopy. PURPOSE: Transversal, descriptive, and analytical study of a quantitative approach with an emphasis on diagnosis. The training test e validation set was composed of images collected from patients who underwent colposcopy. These images were provided by a gynecology clinic located in the city of Criciúma (Brazil). The image database ( n = 170) was divided; 48 images were used for the training process, 58 images were used for the tests, and 64 images were used for the validation. A hybrid neural network based on Kohonen self-organizing maps and multilayer perceptron (MLP) networks was used. Results: After 126 cycles, the validation was performed. The best results reached an accuracy of 72.15%, a sensibility of 69.78%, and a specificity of 68%. Conclusion: Although the preliminary results still exhibit an average efficiency, the present approach is an innovative and promising technique that should be deeply explored in the context of the present study.