Comparison of neural network and k-NN classification methods in medical image and voice recognitions.
We make a comparison of classification ability between BPN (Back Propagation Neural Network) and k-NN (k-Nearest Neighbor) classification methods. Voice data and patellar subluxation images are used. The result was that the average recognition rate of BPN was 9.2 percent higher than that of the k-NN classification method. Although k-NN classification is simple in theory, classification time was fairly long. Therefore, it seems that real time recognition is difficult. On the other hand, the BPN method is long in learning time but is very short in recognition time. Especially if the number of dimensions of the samples is large, it can be said that BPN is better than k-NN in classification ability.
D000465:Algorithms; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D004204:Joint Dislocations / Q000000981:diagnostic imaging*; D007719:Knee Joint / Q000000981:diagnostic imaging*; D008297:Male; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D010700:Phonetics; D014057:Tomography, X-Ray Computed