Cell classification for diagnostic of reactive histocytic hyperplasia using neural networks

Milenovic, D.R.; Stoiljkovic, L.; Stojanović, Nenad

doi:10.1109/melcon.1998.699483

Cited by 2 publications

References 2 publications

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The Identification and Clustering Analysis of Auditory Neurons for Salicylated-Induced Rat Model

Cheng

Chen

Wang

2005

2005 IEEE Engineering in Medicine and Biology 27th Annual Conference

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Salicylate-induced rat model is one of the animal models for tinnitus study. In this study, a radial basis function neural network for automatic identification is firstly developed due to its features of easy training and learning. From the experimental results, the recognition rate is demonstrated to be as high as 98%. Not only the recognition rate is improved, but also it is very objective in analysis. Secondly, a support vector clustering is applied to neurons distribution analysis. Based on the clustering analysis, it is found that the cluster number and distribution area for the Salicylated-induced fos-labeled neurons are very different from those of controlled group.

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The Identification and Clustering Analysis of Auditory Neurons for Salicylated-Induced Rat Model

Cheng

Chen

Wang

2005

2005 IEEE Engineering in Medicine and Biology 27th Annual Conference

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Benchmarking HEp-2 Cells Classification Methods

Foggia

Percannella

Soda

et al. 2013

IEEE Trans. Med. Imaging

224

153

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In this paper, we report on the first edition of the HEp-2 Cells Classification contest, held at the 2012 edition of the International Conference on Pattern Recognition, and focused on indirect immunofluorescence (IIF) image analysis. The IIF methodology is used to detect autoimmune diseases by searching for antibodies in the patient serum but, unfortunately, it is still a subjective method that depends too heavily on the experience and expertise of the physician. This has been the motivation behind the recent initial developments of computer aided diagnosis systems in this field. The contest aimed to bring together researchers interested in the performance evaluation of algorithms for IIF image analysis: 28 different recognition systems able to automatically recognize the staining pattern of cells within IIF images were tested on the same undisclosed dataset. In particular, the dataset takes into account the six staining patterns that occur most frequently in the daily diagnostic practice: centromere, nucleolar, homogeneous, fine speckled, coarse speckled, and cytoplasmic. In the paper, we briefly describe all the submitted methods, analyze the obtained results, and discuss the design choices conditioning the performance of each method.

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Cell classification for diagnostic of reactive histocytic hyperplasia using neural networks

Cited by 2 publications

References 2 publications

The Identification and Clustering Analysis of Auditory Neurons for Salicylated-Induced Rat Model

The Identification and Clustering Analysis of Auditory Neurons for Salicylated-Induced Rat Model

Benchmarking HEp-2 Cells Classification Methods

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