Performance of Machine Learning Classification in Mammography Images
  using BI-RADS
Malitha Gunawardhana Norbert Zolek
This research aims to investigate the classification accuracy of various
state-of-the-art image classification models across different categories of
breast ultrasound images, as defined by the Breast Imaging Reporting and Data
System (BI-RADS). To achieve this, we have utilized a comprehensively assembled
dataset of 2,945 mammographic images sourced from 1,540 patients. In order to
conduct a thorough analysis, we employed six advanced classification
architectures, including VGG19 \cite{simonyan2014very}, ResNet50
\cite{he2016deep}, GoogleNet \cite{szegedy2015going}, ConvNext
\cite{liu2022convnet}, EfficientNet \cite{tan2019efficientnet}, and Vision
Transformers (ViT) \cite{dosovitskiy2020image}, instead of traditional machine
learning models. We evaluate models in three different settings: full
fine-tuning, linear evaluation and training from scratch. Our findings
demonstrate the effectiveness and capability of our Computer-Aided Diagnosis
(CAD) system, with a remarkable accuracy of 76.39\% and an F1 score of 67.94\%
in the full fine-tuning setting. Our findings indicate the potential for
enhanced diagnostic accuracy in the field of breast imaging, providing a solid
foundation for future endeavors aiming to improve the precision and reliability
of CAD systems in medical imaging.