Breast Cancer Classifier

• Coded a Convolutional Neural Network using transfer learning to classify breast cancer images as benign or malignant.
• Used pretrained VGG 16 model and modified last layer.
• Added 2 fully connected layers in the end.

Data Set

Used the breast cancer images provided in the BreakHis data set. It can be requested here: https://web.inf.ufpr.br/vri/databases/breast-cancer-histopathological-database-breakhis/

Data Structure

Original

The original data folder looked like this

Rearranged the dataset as

Libraries used

• numpy
• pandas
• matplotlib
• Opencv
• Keras
• Pytorch

Important Observations

I trained the data for different learning rates and number of epochs. I observed that with learning rate = 0.02 and number of epochs = 25 I get about 85% test accuracy as well as decreasing error graph. If I train it for 30 epochs, the model supoosedely overfits as seen in the test error. For learning rate = 0.001, the error does decrease, however, the accuracy is about 73% only.

Learning Rate	Number of Epochs	Training error	Testing error
0.02	25
0.02	30
0.001	25
0.01	25
0.05	25