Adaptive classification of urinary sediment images using feedback training
Satoshi Mitsuyama, Jun Motoike, Hitoshi Matsuo · Systems and Computers in Japan · 2001
In urinary sediment examination, untypical particles may appear, even though very infrequently. Neural network with feedback training proposed in this study offers improved classification of such untypical particles, which was difficult using conventional classification algorithms. The proposed method suggests that network is optimized using classification results obtained for typical objects. The method may be easily applied to complicated patterns with multiple parameters. Operation and efficiency of the proposed method were confirmed by computer simulation. Additional validation was obtained by applying the proposed method to classification of urinary sediment images. © 2001 Scripta Technica, Syst Comp Jpn, 32(2): 11–18, 2001