Highlights
- •Multi input data classification methodology for Coronary Artery Disease diagnosis.
- •Integration of InceptionV3 network and Random Forest classifier.
- •Deep Learning matched the human expertise in Coronary Artery Disease diagnosis.
Abstract
Purpose
Accurate detection and treatment of Coronary Artery Disease is mainly based on invasive
Coronary Angiography, which could be avoided provided that a robust, non-invasive
detection methodology emerged. Despite the progress of computational systems, this
remains a challenging issue. The present research investigates Machine Learning and
Deep Learning methods in competing with the medical experts' diagnostic yield. Although
the highly accurate detection of Coronary Artery Disease, even from the experts, is
presently implausible, developing Artificial Intelligence models to compete with the
human eye and expertise is the first step towards a state-of-the-art Computer-Aided
Diagnostic system.
Methods
A set of 566 patient samples is analysed. The dataset contains Polar Maps derived
from scintigraphic Myocardial Perfusion Imaging studies, clinical data, and Coronary
Angiography results. The latter is considered as reference standard. For the classification
of the medical images, the InceptionV3 Convolutional Neural Network is employed, while,
for the categorical and continuous features, Neural Networks and Random Forest classifier
are proposed.
Results
The research suggests that an optimal strategy competing with the medical expert's
accuracy involves a hybrid multi-input network composed of InceptionV3 and a Random
Forest. This method matches the expert's accuracy, which is 79.15% in the particular
dataset.
Conclusion
Image classification using deep learning methods can cooperate with clinical data
classification methods to enhance the robustness of the predicting model, aiming to
compete with the medical expert's ability to identify Coronary Artery Disease subjects,
from a large scale patient dataset.
Graphical abstract
Graphical Abstract
Keywords
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Article info
Publication history
Published online: April 23, 2021
Accepted:
April 12,
2021
Received in revised form:
April 2,
2021
Received:
November 9,
2020
Identification
Copyright
© 2021 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.
