Auto-MyIn: Automatic diagnosis of myocardial infarction via multiple GLCMs, CNNs, and SVMs. (February 2023)

• Record Type: Journal Article
• Authors: Attallah, Omneya; Ragab, Dina A.
• Is Part Of: Biomedical signal processing and control. Volume 80(2023)Part 1
• Publication Date: 2023-02
• Subjects: Cardiovascular (CV) Diseases -- Myocardial Infarction (MI) Diagnosis -- Artificial Intelligence (AI) -- Deep Learning -- Convolutional Neural Network (CNN) -- Grey level Co-occurrence Matrix (GLCM) -- Support Vector Machine (SVM); AI Artificial Intelligence -- ANN Artificial Neural Network -- CA Classification Accuracy -- CNN Convolutional Neural Networks -- CV Cardiovascular Diseases -- DE-MRI Delayed-Enhancement Magnetic Resonance -- DOR Diagnostic Odds Ratio -- EF Ejection Fraction -- FN False Negative -- FP False Positive -- GLCM Grey Level Covariance Matrix -- MCC Matthew Correlation Coefficient -- MI Myocardial Infarction -- MRI Magnetic Resonance Imaging -- PCA Principal Component Analysis -- ROC Receiver Operating Characteristic -- ROI Region of Interest -- SVM Support Vector Machine -- TL Transfer Learning -- TN True Negative -- TP True Positive -- YOLO You Only Look Once; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2022.104273
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: The paper proposes an automated to diagnose MI using DE-MRI images called Auto-MyIn. Auto-MyIn utilizes ensemble CNNs of different architectures to extract features from not only one CNN. GLCM textural-based analysis is performed on DE-MRI images, and the results are subsequently fed into CNN models as input. PCA is used by Auto-MyIn to combine the deep features of the various CNN architectures. Prior to classification, Auto-MyIn neither perform segmentation phase nor use the clinical data. Abstract: This paper proposes an automated diagnostic tool namely, Auto-MyIn, for diagnosing myocardial infarction (MI) using multiple convolutional neural networks (CNN). Rather than utilizing the spatial information of the original delayed-enhancement magnetic resonance (DE-MRI) images, Auto-MyIn uses the textural information obtained by applying grey level co-occurrence matrix (GLCM) of four different grey levels to train the three CNNs (ResNet-18, DarkNet-19, and SqueezeNet). First, the images generated from each GLCM grey level are used to train each CNN individually. Next, for each GLCM grey level, the textural-based deep features extracted from the three CNNs are concatenated and used to train several support vector machine (SVM) classifiers. Finally, Auto-MyIn fuses textural-based deep features of the four GLCM grey levels using principal component analysis (PCA). The results of Auto-MyIn indicated that fusing the textural-based deep features of each level of GLCM is better than the end-to-end deep learning classification of the three CNNs trained with each grey level of GLCM images. Furthermore, it showed that fusing textural-based deep features of the four grey levels of the GLCM using PCA has further improvement on diagnostic accuracy. Moreover, the results prove that using textural information is superior to using spatial information of the original DE-MRI images. In addition, the results of Auto-MyIn when compared with other related studies demonstrated its competitive ability. Moreover, the performance of Auto-MyIn shows an accuracy of 0.984, a sensitivity of 0.992, specificity of 0.968, and precision of 0.967, which indicate that it is a reliable tool, therefore it could be employed to help in the clinical decision making and facilitate the diagnostic process of MI thus avoiding the limitations of manual diagnosis.