- The first layer computes distances from the input vector to the training input vectors and produces a vector whose elements indicate how close the input is to a training input. (When you talk about classification between similar looking images, calculation of this distance is an important aspect)
- The second layer sums these contributions for each class of inputs to produce as its net output a vector of probabilities.
- Finally, a compete transfer function on the output of the second layer picks the maximum of these probabilities, and produces a 1 for that class and a 0 for the other classes.
How does the PNN algorithm differentiate between malignant and benign tumors?
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I mean, how does he know the difference between the images in which there are benign tumors and the malignant ones, even if the images look similar. I read a lot of writers and books but can't understand how it works. Thank you in advance!
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Ashu
2022년 11월 30일
Hey Roxana
PNN (Probabilistic Neural Network) is used for classification problems and differentiating between Malignant and Benign Tumors is a problem of that domain.
To get a good classifier which can differentiate between very similar looking images, training the Neural Network is an important aspect.
Here is an architecture of a PNN.
So when you present an input to a PNN
For more information on how to use PNN and how it works you can refer to this link : https://www.mathworks.com/help/deeplearning/ug/probabilistic-neural-networks.html
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