By: Behrouz Rostami, Zeyun Yu Electrical Engineering Department

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Presentation transcript:

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

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

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

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