Improving Computer-Aided Medical Diagnosis Using Generative Adversarial Networks for Carotid Artery Ultrasound Image Data Augmentation and Classification
Authors (s)
(1) * Ricardo Fitas   (Technical University of Darmstadt)  
        Germany
(2)  João Gonçalves   (Technical University of Darmstadt)  
        Germany
(*) Corresponding Author
AbstractCardiovascular diseases are a leading cause of death globally, making early detection of atherosclerosis critical for prevention. Carotid artery ultrasound imaging is a common diagnostic tool; however, the limited availability of labelled medical images hinders the training of deep learning models. This study examines generative adversarial networks (GANs) for data augmentation and classification of carotid artery Doppler images to improve computer-aided medical diagnosis. Four convolutional neural networks (CNNs) – AlexNet, VGGNet, GoogleNet, and CifarNet – are evaluated for their classification performance on original and extended datasets. AlexNet outperforms the other models, achieving a classification accuracy of 94.18% on the extended dataset. The GAN implementation for data augmentation and overfitting reduction demonstrates the potential of generative models in enhancing the performance of deep learning models in medical image analysis, particularly in the "common artery carotid" class. This research contributes to understanding GANs as a valuable tool for data augmentation and classification in the context of carotid artery ultrasound images.
|
Keywords
CNN; GAN; Medical Images; Carotid Artery
Full Text: PDF
Article View
Abstract views : 80 times | PDF files viewed : 44 times10.33650/jeecom.v6i1.8006 |
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Ricardo Fitas
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Journal of Electrical Engineering and Computer (JEECOM)
Published by LP3M Nurul Jadid University, Indonesia, Probolinggo, East Java, Indonesia.