@inproceedings{scholars5763,
           title = {Hep-2 cell images fluorescence intensity classification to determine positivity based on neural network},
         journal = {ISTT 2014 - 2014 IEEE 2nd International Symposium on Telecommunication Technologies},
       publisher = {Institute of Electrical and Electronics Engineers Inc.},
           pages = {138--143},
            note = {cited By 1; Conference of 2nd IEEE International Symposium on Telecommunication Technologies, ISTT 2014 ; Conference Date: 24 November 2014 Through 26 November 2014; Conference Code:115880},
             doi = {10.1109/ISTT.2014.7238192},
            year = {2015},
            isbn = {9781479959822},
          author = {Zazilah, M. and Mansor, A. F. and Yahaya, N. Z.},
        abstract = {This paper applies the concept of Artificial Neural Network (ANN) to classify fluorescence intensity of Hep-2 cell images into three classes; positive, intermediate and negative auto-immune disease. Recently, the recommended method for detection antinuclear auto-antibodies (ANA) is Indirect Immunofluorescence (IIF). The diagnosis consists of estimating fluorescence intensity in the cells. Since the increasing of test demands, trained personnel are not always available for these tasks and the identification of positivity has recently done manually by human analyzing the slide with a microscope, leading to subjective and bad quality results. This work will develop Computer Aided Diagnosis (CAD) tools that can offer a support to physician decision. Then, it discusses image preprocessing, image segmentation and feature extraction. Later, this lead to the proposal of ANN-based classifier that is able to separate essentially the intermediate sample of ANA diseases. The approach has been evaluated using 142 cell images, for 372 training data. The measured performance shows a low overall error rate which is 3 , this is lower than error rate of observed intra-laboratory variability. {\^A}{\copyright} 2014 IEEE.},
        keywords = {Antibodies; Cells; Chemical detection; Cytology; Diagnosis; Feature extraction; Fluorescence; Image classification; Image segmentation; Neural networks, Antinuclear auto-antibodies; Computer Aided Diagnosis(CAD); Fluorescence intensities; Hep-2 cells; Image preprocessing; Image segmentation and feature extractions; Indirect immunofluorescence; Training data, Computer aided diagnosis},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84946595063&doi=10.1109\%2fISTT.2014.7238192&partnerID=40&md5=a9855bb7805e2cf23b6736f1ea0b4d3f}
}