This code helps you classify malignant and benign tumors using Neural Networks.
The example code is in Matlab (R2016 or higher will work).
An ANN is based on a collection of connected units or nodes called artificial neurons (analogous to biological neurons in an animal brain). Each connection (synapse) between neurons can transmit a signal from one to another. The receiving (postsynaptic) neuron can process the signal(s) and then signal downstream neurons connected to it. In common ANN implementations, the synapse signal is a real number, and the output of each neuron is calculated by a non-linear function of the sum of its input. Neurons and synapses may also have a weight that varies as learning proceeds, which can increase or decrease the strength of the signal that it sends downstream. Further, they may have a threshold such that only if the aggregate signal is below (or above) that level is the downstream signal sent.
For more information, see
- Dataset- UCI-ML
- I have used 30 features to classify
- Instead of 0=benign and 1=malignant, I have used 1=benign and 2=malignant
To run the code, type run cancer.m
run cancer.m
- Used a shallow neural net with one hidden layer and 20 units.
- I have used a linear learning rate decay for decreasing cost without overshooting the minima.
- Dataset- UCI-ML
- I have used 30 features to classify
- Instead of 0=benign and 1=malignant, I have used 1=benign and 2=malignant
To run the code, type python B_Cancer_nn.py
python B_Cancer_nn.py