SMU Data Science Review

The Simultaneous Detection and Classification of Mass and Calcification Leading to Breast Cancer in Mammograms

Scott Gozdzialski, Southern Methodist UniversityFollow
Alex Stern, Southern Methodist UniversityFollow
Ireti Fasere, Southern Methodist UniversityFollow
Daniel W. Engels, Southern Methodist UniversityFollow

Abstract

In this paper, we present a novel method for detecting and classifying breast cancer calcification and masses in a single step. The detection and classification steps of calcifications and masses identifiable with a mammogram image are typically performed independently even though their simultaneous solution may lead to a more efficient approach. Our novel method utilizes a Convolutional Neural Network (CNN) to classify the calcifications and masses of different cropped images of a mammogram. We utilize a sliding window detector to break apart full mammogram images into sub-images, and identify and classify the observable objects in the sub-images. We receive multiple probabilities for each sub-image for the different possible classifications. We rank the sub-images, displaying the coordinates of the highest ranked sub-images for each classification. The results of this process are that we detect 46% of cancer within the mammograms and properly classify 64% of the calcifications and masses identified.

Recommended Citation

Gozdzialski, Scott; Stern, Alex; Fasere, Ireti; and Engels, Daniel W. (2019) "The Simultaneous Detection and Classification of Mass and Calcification Leading to Breast Cancer in Mammograms," SMU Data Science Review: Vol. 2: No. 1, Article 10.
Available at: https://scholar.smu.edu/datasciencereview/vol2/iss1/10