Authors:
Adam Mračko
1
;
Ivan Cimrák
1
;
Lucia Vanovčanová
2
;
3
and
Viera Lehotská
2
;
3
Affiliations:
1
Faculty of Management Science and Informatics, University of Žilina, 010 26 Žilina, Slovakia
;
2
2nd Radiology Department, Faculty of Medicine, Comenius University in Bratislava, 813 72 Bratislava, Slovakia
;
3
St. Elizabeth Cancer Institute, 812 50 Bratislava, Slovakia
Keyword(s):
Convolutional Neural Networks, Artificial Intelligence, Mammography, Machine Learning, Breast Calcifications.
Abstract:
Study delves into the application of deep learning models for the classification of breast calcifications in mammography images. Initial objective was to investigate various convolutional neural network (CNN) architectures and their influence on model accuracy. ResNet101 emerged as the most effective architecture, although other models exhibited comparable performances. The insights gained were subsequently applied to the main goal, which focused on examining the transferability of knowledge between models trained on digitalized films (Curated Breast Imaging Subset of Digital Database for Screening Mammograph) and those trained on digital mammography images (Optimam Database). Results confirmed the lack of seamless transferability, prompting the creation of a combined dataset for training, significantly improving overall model accuracy to 76.2%. The study also scrutinized instances of incorrect predictions across different models, particularly those posing challenges even for medical
professionals. Visualizations using Grad-Cam aided in understanding the models’ decision-making process.
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