@article{(Open Science Index):https://publications.waset.org/pdf/8631,
	  title     = {ANN-Based Classification of Indirect Immuno Fluorescence Images},
	  author    = {P. Soda and  G.Iannello},
	  country	= {},
	  institution	= {},
	  abstract     = {In this paper we address the issue of classifying the fluorescent intensity of a sample in Indirect Immuno-Fluorescence (IIF). Since IIF is a subjective, semi-quantitative test in its very nature, we discuss a strategy to reliably label the image data set by using the diagnoses performed by different physicians. Then, we discuss image pre-processing, feature extraction and selection. Finally, we propose two ANN-based classifiers that can separate intrinsically dubious samples and whose error tolerance can be flexibly set. Measured performance shows error rates less than 1%, which candidates the method to be used in daily medical practice either to perform pre-selection of cases to be examined, or to act as a second reader.
},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {2},
	  number    = {8},
	  year      = {2008},
	  pages     = {2752 - 2757},
	  ee        = {https://publications.waset.org/pdf/8631},
	  url   	= {https://publications.waset.org/vol/20},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 20, 2008},
	}