This paper presents a state-of-the-art texture analysis method called “randomized neural network based signature” applied to the classification of pap-smear cell images for the Papanicolaou test. For this purpose, we used a well-known benchmark dataset composed of 917 images and compared the aforementioned image signature to other texture analysis methods. The obtained results were promising, presenting accuracy of 87.57% and AUC of 0.8983 using LDA and SVM, respectively. These performance values confirm that the randomized neural network based signature can be applied successfully to this important medical problem.
CITATION STYLE
de Mesquita Sá Junior, J. J., Backes, A. R., & Bruno, O. M. (2018). Pap-smear image classification using randomized neural network based signature. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10657 LNCS, pp. 677–684). Springer Verlag. https://doi.org/10.1007/978-3-319-75193-1_81
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