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21 May 1999 Automatic classification of urinary sediment images by using a hierarchical modular neural network
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Abstract
We have developed an automated image-classification method for the examination of urinary sediment. Urine contains many kinds of particles of various colors and sizes. To classify these particles automatically, we developed a hierarchical modular neural network (HMNN) to enable accurate classification of urinary-sediment images. Simulations results showed that a neural network with a modular structure can classify artificially generated patterns more accurately than a single neural network (SNN). By using a HMNN, any kind of particle contained in urine can be automatically classified. We compared the classification accuracy when using the HMNN to that with a SNN and found that the classification accuracy for some classes of particles when using the HMNN was 25% to 30% higher than when using the SNN. With the HMNN, the examination accuracy was sufficient to allow automation of the examination process.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Satoshi Mitsuyama, Jun Motoike, and Hitoshi Matsuo "Automatic classification of urinary sediment images by using a hierarchical modular neural network", Proc. SPIE 3661, Medical Imaging 1999: Image Processing, (21 May 1999); https://doi.org/10.1117/12.348624
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