Analysis of The Utilization of The Automatic Exposure Control (AEC) Feature in The Use of Deep Learning Breast Image Technology in Women's Mammogram Screening Examinations at Dharmais Cancer Hospital

Abstract

Deep learning technology is useful for radiology specialists as double reading to help increase the accuracy of image interpretation results. One of the preparations for maximizing the use of this technology is using good-quality images as the source. The Automatic Exposure Control (AEC) feature, which functions to determine exposure factors automatically, is expected to help produce images with good and consistent quality so that deep learning technology can work more effectively. This research aims to determine the quality results of mammogram images taken using the AEC feature and to analyze the use of deep learning technology in evaluating mammogram images. This research method is retrospective by collecting 800 mammogram images randomly and anonymously. Three hundred images were tested, 500 were evaluated, and 250 were analyzed for image quality based on references related to applying AEC and assessing the contrast-to-noise ratio (CNR). Deep learning technology was analyzed by comparing the results of mammogram image evaluation using deep learning and the evaluation results of a radiology specialist. Deep learning technology analysis shows that 98% of mammograms have the same results as the radiology doctor's evaluation, and 2% have different results from the radiology doctor's evaluation where the image has a dense breast type. The image quality results in this research showed that 97.6% of the 250 samples taken using the AEC feature had good image quality, and 2.4% had poor image quality due to inappropriate breast positioning during the examination.