Title:Deep Learning Mammography Classification with a Small Set of Data
Volume: 20
Author(s): Epimack Michael*, He Ma and Palme Mawagali
Affiliation:
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China
Keywords:
Breast cancer, Mammogram, MIAS dataset, Convolutional neural network, Computer-aided diagnosis, Classification.
Abstract:
Background:
Breast cancer is one of the leading causes of mortality among women. In addition, 1 in 8 women and 1 in 833 men will be diagnosed with breast
cancer in 2022. The detection of breast cancer can not only lower treatment costs but also increase survival rates. Due to increased cancer
awareness, more women are undergoing breast cancer screening, leading to more cases being diagnosed worldwide, but doctors' ability to analyze
these images is limited. As a result, they get overloaded leading to misinterpretations. The advent of computer-aided diagnosis (CAD) minimized
man’s involvement and achieved good results. CAD helps medical doctors automatically detect and analyze abnormalities found in the breast.
Such abnormalities may be benign or malignant tumors.
Objective:
The goal of this study is to evaluate the effectiveness of using seven layers to classify breast cancer as either benign or malignant using
mammograms.
Materials and Methods:
The open-source MIAS dataset of 322 images was used for our study, of which 207 were normal images and 115 were abnormal images. The
proposed CNN model convolves an image into seven layers that extract features from the input images, and these features are used to classify
breast cancer as malignant or benign.
Results:
The proposed CNN used a limited data set and achieved the best result compared to previous work. The method achieved results with a 0.39% loss,
99.89% accuracy, 99.85% precision, 99.89% recall, 99.87% F1-score, and an area under the curve noted to be 100.0%.
Conclusion:
CNN uses a small amount of data to determine abnormalities; the method will assist a medical doctor in determining whether or not a specific
patient has cancer.
