CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learning
Journal:
arXiv
Published Date:
Mar 7, 2025
Abstract
Cardiac ultrasound (US) scanning is a commonly used techniques in cardiology
to diagnose the health of the heart and its proper functioning. Therefore, it
is necessary to consider ways to automate these tasks and assist medical
professionals in classifying and assessing cardiac US images. Machine learning
(ML) techniques are regarded as a prominent solution due to their success in
numerous applications aimed at enhancing the medical field, including
addressing the shortage of echography technicians. However, the limited
availability of medical data presents a significant barrier to applying ML in
cardiology, particularly regarding US images of the heart. This paper addresses
this challenge by introducing the first open graded dataset for Cardiac
Assessment and ClassificaTion of UltraSound (CACTUS), which is available
online. This dataset contains images obtained from scanning a CAE Blue Phantom
and representing various heart views and different quality levels, exceeding
the conventional cardiac views typically found in the literature. Additionally,
the paper introduces a Deep Learning (DL) framework consisting of two main
components. The first component classifies cardiac US images based on the heart
view using a Convolutional Neural Network (CNN). The second component uses
Transfer Learning (TL) to fine-tune the knowledge from the first component and
create a model for grading and assessing cardiac images. The framework
demonstrates high performance in both classification and grading, achieving up
to 99.43% accuracy and as low as 0.3067 error, respectively. To showcase its
robustness, the framework is further fine-tuned using new images representing
additional cardiac views and compared to several other state-of-the-art
architectures. The framework's outcomes and performance in handling real-time
scans were also assessed using a questionnaire answered by cardiac experts.
