1. Breast cancer tumor classification using deep convolutional neural networks
By:
Behrouz Rostami, Zeyun Yu
Electrical Engineering Department
University of Wisconsin - Milwaukee
April 2018

2. Introduction Breast cancer:
A type of cancer with highest incidence rates in women
Most common cause of cancer death in women
Affects one of every ten women in Europe and one of every eight in the US
Early detection and treatment; most effective methods of reducing mortality
Mammography; a manual process, prone to human error
Main advantage of deep learning models: high-level features compared to hand-built features
Challenges: need to have large training sets pre-trained deep learning models

3. Pretrained AlexNet topology
Approach
Step 1 - Preprocessing:
Flipping, thresholding, Contrast enhancement
Removing the muscle part from the image
Step 2 - Classification:
Using a pre-trained AlexNet topology with 25 hidden layers
Final output: A confusion matrix with 3 labels, Benign, Malignant, and Normal
Pretrained AlexNet topology
Original image
Image after
preprocessing

4. Results Our results – confusion matrix The reference paper results
Benign
Malignant
Normal
0.8029
0.0273
0.1696
0.0369
0.8345
0.1285
0.1279
0.1577
0.7142
Our results – confusion matrix
The reference paper results