Original scientific paper

https://doi.org/10.17559/TV-20211221131838

A Decision-Making Tool for Early Detection of Breast Cancer on Mammographic Images

Duygu Çelik Ertuğrul orcid.org/0000-0003-1380-705X ; Department of Computer Engineering, Engineering Faculty, Eastern Mediterranean University, Famagusta, North Cyprus, via Mersin-10, Turkey
Soona Ahmed Abdullah ; Department of Computer Science and IT, College of Science, Salahaddin University, Erbil, Iraq

Abstract

Breast cancer is one of the most dangerous types of cancer in the world among females. In the medical industry, the early detection of a breast abnormality in a mammogram can significantly decrease the death rate caused by breast cancer. Therefore, researchers directed their focus and efforts to find better solutions. Whereas researchers earlier used semi-automatic algorithms of machine learning, recently the attention is redirected toward deep learning algorithms that automatically extract features. Therefore, in the research study, two pre-trained Convolutional Neural Network models, VGG16 and ResNet50, have been used and applied on mammogram images to classify their abnormalities in terms of (1) the Benign Calcification, (2) the Malignant Calcification, (3) the Benign Mass, and (4) the Malignant Mass. The mammographic images of the CBIS-DDSM dataset are used. In the training phase, various experiments are performed on ROI images to decide on the best model configuration and fine-tuning depth. The experimental results showed that the VGG16 model provided a remarkable advancement over the ResNet50 model; the accuracy obtained was 80.0% in the first model whereas the second model could classify with a 60.0% accuracy almost randomly. Apart from accuracy, the other performance metrics used in this study are precision, recall, F1-Score and AUC. Our evaluation, based on these performance metrics, shows that accurate detection effect is obtained from the two networks with VGG16 being the most accurate. Finally, a decision support tool is developed which classifies the full mammogram images based on the fine-tuned VGG16 architecture into Benign Calcification, Malignant Calcification, Benign Mass, and Malignant Mass.

Keywords

breast cancer; decision support systems; image classification; mammogram images; Resnet50; VGG16

Hrčak ID:

281665

URI

https://hrcak.srce.hr/281665

Publication date:

15.10.2022.

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