Abstract
Polycystic Ovary Syndrome (PCOS) is an endocrine abnormality that occurred in the female reproductive cycle. In general, the approaches to detect PCO follicles are (1) stereology and (2) feature extraction and classification. In Stereology, two-dimensional images are viewed as projections of three-dimensional objects. In this paper, we use the second approach, namely Gabor Wavelet as a feature extractor and a modified backpropagation as a classifier. The modification of backpropagation algorithm which is proposed, namely Levenberg - Marquardt optimization and Conjugate Gradient - Fletcher Reeves to improve the convergence rate. Levenberg - Marquardt optimization produce the higher accuracy than Conjugate Gradient - Fletcher Reeves, but it has a drawback of running time. The best accuracy of Levenberg - Marquardt is 93.925% which is gained from 33 neurons and 16 vector feature and Conjugate Gradient - Fletcher Reeves is 87.85% from 13 neurons and 16 vector feature.
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Wisesty, U.N., Nasri, J., Adiwijaya (2017). Modified Backpropagation Algorithm for Polycystic Ovary Syndrome Detection Based on Ultrasound Images. In: Herawan, T., Ghazali, R., Nawi, N.M., Deris, M.M. (eds) Recent Advances on Soft Computing and Data Mining. SCDM 2016. Advances in Intelligent Systems and Computing, vol 549. Springer, Cham. https://doi.org/10.1007/978-3-319-51281-5_15
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DOI: https://doi.org/10.1007/978-3-319-51281-5_15
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