Digital Library[ Search Result ]
Cardiovascular Disease Prediction using Single-Lead ECG Data
Chaeyoon Park, Gihun Joo, Suhwan Ji, Junbeom Park, Junho Baek, Hyeonseung Im
http://doi.org/10.5626/JOK.2024.51.10.928
The most representative approach to diagnosing cardiovascular disease is to analyze electrocardiogram (ECG), and most ECG data measured in hospitals consist of 12 leads. However, wearable healthcare devices usually measure only single-lead ECG, which has limitations in diagnosing cardiovascular disease. Therefore, in this paper, we conducted a study to predict common cardiovascular diseases such as atrial fibrillation (AF), left bundle branch block (LBBB), and right bundle branch block (RBBB) using a single lead that could be measured with a wearable healthcare device. For experiments, we used a convolutional neural network model and measured its performance using various leads in terms of AUC and F1-score. For AF, LBBB, and RBBB, average AUC values were 0.966, 0.971, and 0.965, respectively, and average F1-scores were 0.867, 0.816, and 0.848, respectively. These experimental results confirm the possibility of diagnosing cardiovascular disease using only a single lead ECG that can be obtained with wearable healthcare devices.
A Survey of Advantages of Self-Supervised Learning Models in Visual Recognition Tasks
Euihyun Yoon, Hyunjong Lee, Donggeon Kim, Joochan Park, Jinkyu Kim, Jaekoo Lee
http://doi.org/10.5626/JOK.2024.51.7.609
Recently, the field of teacher-based artificial intelligence (AI) has been rapidly advancing. However, teacher-based learning relies on datasets with specified correct answers, which can increase the cost of obtaining these correct answers. To address this issue, self-supervised learning, which can learn general features of photos without needing correct answers, is being researched. In this paper, various self-supervised learning models were classified based on their learning methods and backbone networks. Their strengths, weaknesses, and performances were then compared and analyzed. Photo classification tasks were used for performance comparison. For comparing the performance of transfer learning, detailed prediction tasks were also compared and analyzed. As a result, models that only used positive pairs achieved higher performance by minimizing noise than models that used both positive and negative pairs. Furthermore, for fine-grained predictions, methods such as masking images for learning or utilizing multi-stage models achieved higher performance by additionally learning regional information.
Improvement Study on Active Learning-based Cross-Project Defect Prediction System
http://doi.org/10.5626/JOK.2023.50.11.931
This study proposes a practical improvement method for an active learning-based system for cross-project defect prediction. A previous study applied active learning tech- niques to practically improve the performance of cross-project defect prediction, but it used a traditional machine learning model that used hand-made features as input for active learning target selection and defect prediction, therefore feature extraction was expensive and performance was limited. In addition, the problem of performance deviation according to the selection of the input project remained. In this study, the following methods were proposed to overcome these limitations. First, we used a deep learning model that can use the source code as an input to lower the model building cost and improve prediction performance. Second, a Bayesian convolutional neural network is applied to select an active learning target using a deep learning model. Third, instead of considering a single source project, we applied a method that automatically extracts a training data set from multiple projects. Applying the system proposed in this study to 7 open source projects improved the average prediction performance by 13.58% compared to the previous latest research.
R-FLHE: Robust Federated Learning Framework Against Untargeted Model Poisoning Attacks in Hierarchical Edge Computing
http://doi.org/10.5626/JOK.2023.50.1.94
Federated learning is a server-client based distributed learning strategy that collects only trained model to guarantee data privacy and reduce communication costs. Recently, research is being conducted to prepare for the future IoT ecosystem by combining edge computing and federated learning. However, research considering vulnerabilities and threat is insufficient. In this paper, we propose Robust Federated Learning in Hierarchical Edge computing (R-FLHE), a federated learning framework for robust global model from untargeted model poisoning attacks. R-FLHE can aggregate models learned from clients, evaluate them on the edge server, and score them based on the calculated model’s loss. R-FLHE can maintain robustness of the global model by sending only the model of the edge server with the best score to the cloud server. The R-FLHE proposed in this paper shows robustness in maintaining constant performance for each federated learning round, with performance drop of only 0.81% and 1.88% on average even if attacks occur.
Automatic Classification of Pneumonia Based on Ensemble Deep Learning Model Using Intensity Normalization and Multiscale Lung-Focused Patches on Chest X-Ray Images
Yoon Jo Kim, Jinseo An, Helen Hong
http://doi.org/10.5626/JOK.2022.49.9.677
It is difficult to classify normal and pneumonia in pediatric chest X-ray (CXR) images due to irregular intensity values. In addition, deep learning model has a limitation in that it can misclassify CXR by incorrectly focusing on the outer part of the lung. This study proposed an automatic classification of pneumonia based on ensemble deep learning model using three intensity normalizations and multiscale lung-focused patches on CXR images. First, to correct for irregular intensity values in internal lungs, three intensity normalization methods were performed respectively. Second, to focus on internal lungs, regions of interest were extracted by segmenting lung regions. Third, multiscale lung-focused patches were extracted to train the characterization of pneumonia. Finally, ensemble modeling with attention module was performed to improve the classification performance. In the experiment, the method using large patches of CLAHE images showed an accuracy of 92%, which was 5% higher than that of original images. Furthermore, the proposed method using an ensemble of large and middle patches showed the best performance with an accuracy of 93%.
Estimation of Finger Motion using Transient EMG Signals
http://doi.org/10.5626/JOK.2022.49.2.157
In this paper, we propose a deep learning model for estimating finger movements based on EMG signals. We have also evaluated and analyzed the accuracy of the model. We have applied the U-Net structure, which is widely used in medical image analysis, to our model. In general, U-Net is mainly used for processing of two-dimensional images. However, in this paper, 8-channel one-dimensional time series EMG data is used as inputs, and information about finger movement is obtained as results. We have acquired the data set consisting of 8,000 motions, which is divided into the training and evaluation data sets. The accuracy of the prediction of our model is about 89.32%.
Detecting Design Infringement Using Multi-Modal Visual Data and Auto Encoder based on Convolutional Neural Network
Jeonggeol Kim, Jiyou Seo, Chanjae Lee, Seongmin Jo, Seungmin Kim, Seokmin Yoon, Young Yoon
http://doi.org/10.5626/JOK.2022.49.2.137
Recently, it has become very difficult to distinguish between counterfeit products and authentic goods, and the volume of these forgeries is increasing at an alarming rate. Prompt detection of these counterfeit products is challenging since only humans can identify these forgeries through trained expertise. In this paper, given the photograph and design drawing, we use convolutional neural networks and auto-encoders to detect the possible infringement of design rights without dissembling or damaging the suspected items. We have developed an easy-to-expand system that supports the constant addition of new goods to be examined. We present the result of our system tested with a set of authentic and forged goods.
Automatic Pancreas Segmentation Based on Cascaded Network Considering Pancreatic Uncertainty in Abdominal CT Images
Hyeon Dham Yoon, Hyeonjin Kim, Helen Hong
http://doi.org/10.5626/JOK.2021.48.5.548
Pancreas segmentation from abdominal CT images is a prerequisite step for understanding the shape of the pancreas in pancreatic cancer detection. In this paper, we propose an automatic pancreas segmentation method based on a deep convolutional neural network(DCNN) that considers information about the uncertain regions generated by the positional and morphological diversity of the pancreas in abdominal CT images. First, intensity and spacing normalizations are performed in the whole abdominal CT images. Second, the pancreas is localized using 2.5D segmentation networks based on U-Net on the axial, coronal, and sagittal planes and by combining through a majority voting. Third, pancreas segmentation is performed in the localized volume using a 3D U-Net-based segmentation network that takes into account the information about the uncertain areas of the pancreas. The average DSC of pancreas segmentation was 83.50%, which was 10.30%p, 10.44%p, 6.52%p, 1.14%p, and 3.95%p higher than the segmentation method using 2D U-Net at axial view, coronal view, sagittal view, majority voting of the three planes, and 3D U-Net at localized volume, respectively.
An Evaluation Method for Generalization Errors of CNN using Training Data
http://doi.org/10.5626/JOK.2021.48.3.284
Even with high-performance CNNs, generalization errors, which are the errors on test datasets that are expected in the real world, are often high. This generalization error must be reduced so that the model can maintain its learned performance in the real world. This paper defines a response set as a neuron set that is frequently activated for each model class learned from the training dataset with high data diversity. Also, the differences in generalization errors due to the data diversity of the test dataset are considered. The difference is defined as a relative generalization error. In the current work, an evaluation method for CNN generalization error using only the training dataset is proposed by using the relationship between the CNN class response set and the relative generalization error. The case study confirms that the response set ratio is related to the relative generalization error and demonstrates the effectiveness of the evaluation method for generalization errors of CNN using training data.
CNN-based Reduced Complexity Decision Confidence Estimation for Imbalanced Web Application Attack Detection
Seungyoung Park, Hansung Kim, Taejoon Jung
http://doi.org/10.5626/JOK.2020.47.9.842
As web application attacks have been rapidly increasing and their types have been diversified, there are limitations on detecting them with the existing schemes. To resolve this problem, the detection techniques using machine learning such as the convolutional neural network (CNN) have been proposed. However, the confidence on the decision error sample in these techniques has been unreliable. To estimate more reliable decision confidence, the Monte-Carlo batch normalization (MCBN) technique combined with the CNN has been proposed. In particular, the CNN performs multiple decisions on a given evaluation sample using multiple mini-batches containing it. Then, its decision confidence estimate is obtained by averaging the multiple decision results. However, it requires too large of a computational load. The reason is that each mini-batch comprises randomly selected (M-1) training samples and only one evaluation sample, when the mini-batch size is M. In this paper, we propose a reduced complexity decision confidence estimation scheme for imbalanced web application attack detection. Specifically, the proposed scheme reduces the computational load by up to M times compared to the MCBN scheme. Also, at the estimation process, the ratio of normal and attack samples in the mini-batch should be maintained the same as that of the training process. To achieve this, we found which class size was small by performing a temporal decision on the evaluation samples. Then, the small class was over-sampled using the training samples to maintain the ratio. Our experimental results showed that the performance improved, and the reliability estimation performance was not significantly degraded compared to the MCBN scheme.
Search
Journal of KIISE
- ISSN : 2383-630X(Print)
- ISSN : 2383-6296(Electronic)
- KCI Accredited Journal
Editorial Office
- Tel. +82-2-588-9240
- Fax. +82-2-521-1352
- E-mail. chwoo@kiise.or.kr