Automatic classification of urinary sediment images by using a hierarchical modular neural network

Satoshi Mitsuyama; Jun Motoike; Hitoshi Matsuo

doi:10.1117/12.348624

21 May 1999 Automatic classification of urinary sediment images by using a hierarchical modular neural network

Satoshi Mitsuyama, Jun Motoike, Hitoshi Matsuo

Proceedings Volume 3661, Medical Imaging 1999: Image Processing; (1999) https://doi.org/10.1117/12.348624
Event: Medical Imaging '99, 1999, San Diego, CA, United States

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.

Citation Download Citation

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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