Breast Cancer Classifier (Shallow Network) 🔬

This code helps you classify malignant and benign tumors using Neural Networks.

Code Requirements 🦄

The example code is in Matlab (R2016 or higher will work).

Description 🧪

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

Notes 🗒️

1. Dataset- UCI-ML
2. I have used 30 features to classify
3. Instead of 0=benign and 1=malignant, I have used 1=benign and 2=malignant

Results 📊

Execution 🐉

To run the code, type run cancer.m

run cancer.m

Python Implementation 👨‍🔬

• 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.

1. Dataset- UCI-ML
2. I have used 30 features to classify
3. Instead of 0=benign and 1=malignant, I have used 1=benign and 2=malignant

Results 📊

Execution 🐉

To run the code, type python B_Cancer_nn.py

python B_Cancer_nn.py