Arrhythmia detection model using modified DenseNet for comprehensible Grad-CAM visualization
- Authors
- Kim, Jin-Kook; Jung, Sunghoon; Park, Jinwon; Han, Sung Won
- Issue Date
- Mar-2022
- Publisher
- ELSEVIER SCI LTD
- Keywords
- Arrhythmia classification; Electrocardiogram; Class activation mapping; Convolutional neural network
- Citation
- BIOMEDICAL SIGNAL PROCESSING AND CONTROL, v.73
- Indexed
- SCIE
SCOPUS
- Journal Title
- BIOMEDICAL SIGNAL PROCESSING AND CONTROL
- Volume
- 73
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/140847
- DOI
- 10.1016/j.bspc.2021.103408
- ISSN
- 1746-8094
- Abstract
- Diagnosing arrhythmia is difficult, requires significant efforts. Because arrhythmia can be associated with serious diseases, it is important to classify arrhythmia patients with high accuracy, and the basis for the classification model's judgment should be properly demonstrated. Traditional algorithm methods are less accurate, and simply using a high-accuracy image classification deep learning model yields incomprehensible results when the model is visualized with gradient-weighted class activation mapping (Grad-CAM). We want to achieve highperformance deep learning models can also comprehensible visualization. To obtain this, two hypotheses about Grad-CAM were established and the experiment was conducted. As a result, a method that could clearly visualize the response area using Grad-CAM with a higher classification performance of 0.98 accuracy is created.
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Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
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