# View Classification and Object Detection in Cardiac Ultrasound to Localize Valves via Deep Learning Source: [View Classification and Object Detection in Cardiac Ultrasound to Localize Valves via Deep Learning]() Collection index: [llms.txt]() Data updated: 2025-10-26T01:57:41.618Z Authors: Derya Gol Gungor, Bimba Rao, Cynthia Wolverton, Ismayil Guracar Published: 2023-10-31T18:16:02.000Z ## Abstract Echocardiography provides an important tool for clinicians to observe the function of the heart in real time, at low cost, and without harmful radiation. Automated localization and classification of heart valves enables automatic extraction of quantities associated with heart mechanical function and related blood flow measurements. We propose a machine learning pipeline that uses deep neural networks for separate classification and localization steps. As the first step in the pipeline, we apply view classification to echocardiograms with ten unique anatomic views of the heart. In the second step, we apply deep learning-based object detection to both localize and identify the valves. Image segmentation based object detection in echocardiography has been shown in many earlier studies but, to the best of our knowledge, this is the first study that predicts the bounding boxes around the valves along with classification from 2D ultrasound images with the help of deep neural networks. Our object detection experiments applied to the Apical views suggest that it is possible to localize and identify multiple valves precisely. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()