In this paper, we present a Computer Aided Diagnosis that implements a supervised approach to discriminate vessels versus tubules that are two different types of structural elements in images of biopsy tissue. In particular, in this work we formerly describe an innovative preliminary step to segment region of interest, then the procedure to extract from them significant features and finally present and discuss the Back Propagation Neural Network binary classifier performance that shows Precision 91 % and Recall 91 %.
CITATION STYLE
Bevilacqua, V., Pietroleonardo, N., Triggiani, V., Gesualdo, L., Di Palma, A. M., Rossini, M., … Mastrofilippo, N. (2015). Neural network classification of blood vessels and tubules based on haralick features evaluated in histological images of kidney biopsy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9227, pp. 759–765). Springer Verlag. https://doi.org/10.1007/978-3-319-22053-6_81
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