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Deep learning based gastric lesion classification system using data augmentation

Transactions of the Korean Institute of Electrical Engineers 2020年第7期69卷 1033-1039页

作者： Lee, Sin-Ae Kim, Dong-Hyun Cho, Hyun-Chong Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Dept. of Electronics Engineering Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

Gastrointestinal symptoms and functional gastrointestinal disorders comprise a large proportion of primary care and gastroenterology practice. We propose a Computer-aided Diagnosis (CADx) system that analyzing the traditional gastroscope images and help the medical experts improve the accuracy of medical diagnosis. To improve the performance of the CADx system, a data augmentation has also been implemented to increase both the amount and the diversity of the training images. Augmentation method finds the enhancement parameters through RNN through large-scale verified three data, ImageNet, SHVN and CIFAR-10. In this study, we compared the performance of applying data augmentation method using four networks, Inception-V3, Resnet-101, Xception, and Inception-Resnet-V2. For Inception-V3, Resnet-101, Xception, and Inception-Resnet-V2 in normal and abnormal classification, the highest Az values were 0.87, 0.85, 0.88 and 0.82 respectively. The Xception networks and CIFAR-10 data is a promising CADx configuration for gastric lesion which had relatively simple structure and good classification performance. © Korean Institute of Electrical Engineers

关键词： Computer aided diagnosis

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Detecting abnormal behavior of cattle based on object detection algorithm

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Transactions of the Korean Institute of Electrical Engineers 2020年第3期69卷 468-473页

作者： Chae, Jung-Woo Cho, Hyun-Chong Dept. of Electronics Engineering Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

Detection of abnormal behavior and unusual signs of cattle has been an important factor for managers in the livestock farming. Among them, it is of great interest for livestock farmers to detect estrus of cattle, which are unusual behaviors related to breeding management. Most studies on detecting estrus combined with IT technologies have been based on sensors attached to the neck or leg of cattle. But sensor-type devices have the potential to cause stress on the cattle by attaching them, and there are additional cost issues for maintenance. In this study, to solve the above problems, we present a estrus detection system which used regular CCTV and applied the object detection algorithm based on the deep learning model. To run real-time detection in video stream, the activity detection is performed within a single image frame and makes prediction with a single network evaluation. The results of the proposed system showed 97% precision and 96% accuracy. Copyright © The Korean Institute of Electrical Engineers.

关键词： Object detection

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A New Image Augmentation Method for Improving Computer-aided Diagnosis System Performance

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Transactions of the Korean Institute of Electrical Engineers 2021年第1期70卷 102-107页

作者： Lee, Sin-Ae Cho, Hyun-Chong Interdisciplinary Graduate Program for Bit Medical Convergence Kangwon National University Korea Republic of Dept. of Electronics Engineering and Interdisciplinary Graduate Program for Bit Medical Convergence Kangwon National University Korea Republic of

Gastric cancer is the largest percentage of cancer cases in Korea. A precise way to find the occurrence of gastrointestinal diseases is through gastroscopy by a trained diagnostic physician. Computer-aided diagnosis (CADx) system helps improve the reliability and speed of diagnosis. Hie CADx system has developed with deep learning, which is data dependent. However, medical image data is labor intensive and time consuming, making it difficult for large data sets to be formed. To solve this problem, it is important to apply augmentation techniques. In tliis paper, we propose an augmentation method which is suitable for the data. A basic classification model was made by leaning a data set consisting of the original images. Each of the 14 augment technique-applied data set was input into the generated model and the fl-score values were compared. Hie fl-score of the highest performance among the proposed methods, was 0.9221, with an increase of about 0.085. © 2021 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Computer aided diagnosis

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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An Evaluation of Energy-efficient AI Models for Edge Deployment in 12-lead ECG Classification 13

An Evaluation of Energy-efficient AI Models for Edge Deploym...

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13th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2024

作者： Baskoro, Angger Lin, Yu-Chi Nuannimnoi, Sirapop Rijal, Himmatur Traore, Gnangze Elie Gildas Azhar, Milzam Waf Huang, Ching-Yao National Yang Ming Chiao Tung Univ. Eecs International Graduate Program Hsinchu Taiwan Institute of Electronics National Yang Ming Chiao Tung Univ. Hsinchu Taiwan National Yang Ming Chiao Tung Univ. Department of Electronics Engineering Hsinchu Taiwan

ISBN: (纸本)9798350368086

In recent years, artificial intelligence (AI) techniques, particularly deep learning, have shown significant promise in modern tele-medicine. Deep learning models, particularly Convolutional Neural Networks (CNNs), have been trained on large datasets to identify patterns and anomalies within 12-lead electrocardiograms (ECGs), which are crucial for diagnosing various cardiac conditions. In this study, we conducted a comparative analysis to identify and evaluate models that can be effectively deployed in rural healthcare settings, where energy efficiency, low computational requirements, and ease of use are critical factors. We compare the performance of two well-known sequential data processing model architectures: time-series RetNet and Mamba. The best model from this comparative study achieves a notable precision of over 95% in a 10 fold cross-validation for the classification of nine types of cardiac arrhythmias (CA) in the 2018 PhysioNet/CinC Challenge (CPSC 2018) dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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Eye-Zheimer: A Deep Transfer Learning Approach of Dementia Detection and Classification from NeuroImaging 7

Eye-Zheimer: A Deep Transfer Learning Approach of Dementia D...

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7th IEEE International Conference on engineering Technologies and Applied Sciences, ICETAS 2020

作者： Alon, Helcy D. Ligayo, Michael Angelo D. Misola, Maribel A. Sandoval, Allan A. Fontanilla, Marites V. Technological Institute of the Philippines Graduate Program Manila Philippines Quezon City University Department of Electronics Engineering Quezon City Philippines FEU Institute of Technology Department of Computer Engineering Manila Philippines St. Paul University Department of Information Technology Quezon City Philippines

ISBN: (纸本)9780738105048

Dementia is a common term for memory loss, speech, problem-solving, and other cognitive skills that are serious enough to interfere with everyday life, and Alzheimer's is the leading cause of dementia. Alzheimer's disease is presumed to develop 20 years or more before symptoms occur, with degenerative changes that are unapparent to the person affected. The deep learning approach for early detection and Alzheimer's disease classification has recently gained significant attention. This study proposed disease detection trained by utilizing the YOLO v3 algorithm that aims to detect Alzheimer's disease based solely on Magnetic Resonance Imaging (MRI). Pascal VOC format and LabelImg tool are used for annotating the datasets, categorizing the image as non-demented and mild-demented. Model 4 was used in the system having 98.617% training accuracy, 98.8207% validation accuracy, and mAP of 96.17%. To test the accuracy of the used model, images of MRI scans are presented and it recorded 80% testing accuracy. © 2020 IEEE.

关键词： Object detection

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SCENE TEXT RECOGNITION MODELS EXPLAINABILITY USING LOCAL FEATURES

arXiv

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arXiv 2023年

作者： Ty, Mark Vincent Atienza, Rowel Electrical and Electronics Engineering Institute University of the Philippines Philippines AI Graduate Program University of the Philippines Philippines

Explainable AI (XAI) is the study on how humans can be able to understand the cause of a model's prediction. In this work, the problem of interest is Scene Text Recognition (STR) Ex-plainability, using XAI to understand the cause of an STR model's prediction. Recent XAI literatures on STR only provide a simple analysis and do not fully explore other XAI methods. In this study, we specifically work on data explainability frameworks, called attribution-based methods, that explain the important parts of an input data in deep learning models. However, integrating them into STR produces inconsistent and ineffective explanations, because they only explain the model in the global context. To solve this problem, we propose a new method, STRExp, to take into consideration the local explanations, i.e. the individual character prediction explanations. This is then benchmarked across different attribution-based methods on different STR datasets and evaluated across different STR models. © 2023, CC BY-SA.

关键词： Forecasting

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Behavior-Based Control with Learning from Demonstration for Path Following Applied to Mobile Robots Soccer

Behavior-Based Control with Learning from Demonstration for ...

引用

IEEE Latin American Robotics Symposium, LARS

作者： Matheus de S. Luiz M. A. Pastrana Guilherme Achilles de Oliveira e Aguiar R. B. Costa Luiz F. Rodrigues e Silva Faculty of Gama Electronics Engineering Undergraduate Program Department of Mechanical Engineering Mechatronics Graduate Program Faculty of Technology Mechatronics Engineering Undergraduate Program Faculty of Gama Aerospace Engineering Undergraduate Program University of Brasilia Brasilia DF Brazil

ISBN: (数字)9798331508807

ISBN: (纸本)9798331508814

Learning from Demonstration (LfD) enables robots to learn new tasks through an imitation process. This methodology can be combined with Behavior-Based Control (BBC), which facilitates the abstraction of behaviors for the robot by dividing them into micro-behaviors. These can be implemented using Artificial Neural Networks trained by bio-inspired algorithms. In this work, a novel methodology based on the LfD and BBC methods is proposed for controlling soccer robots, specifically for the Very Small Size Soccer (VSSS) category, which demands quick and dynamic responses. Simulations using CoppeliaSim were conducted to demonstrate how the proposed methodology can be used for path planning and path-following problems in the IEEE VSSS category.

关键词： Measurement Training Heuristic algorithms Artificial neural networks Turning Robustness Path planning Robots Optimization Sports

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TAST-Trademark Application Assistant

TAST-Trademark Application Assistant

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IEEE International Conference on Image Processing

作者： P.W.H. Kwan K. Toraichi K. Kameyama F. Kawazoe K. Nakamura Doctoral Program Graduate School of Engineering University of Tsukuba Japan Institute of Information Sciences and Electronics University of Tsukuba Japan Doctoral Program Graduate School of Systems and Information Engineering University of Tsukuba Japan

In this paper, a Trademark Application Assistant (TAST) that aims to speed up the process of a successful trademark application is proposed. The core of TAST is a similarity-based image retrieval system that makes use of relaxation matching on extracted contour segments. A novelty of this research lies in the processing architecture introduced for adapting the highly effective but computation expensive relaxation matching algorithm to real-time image retrieval. Preliminary results illustrating the quality of matching and the scalability of the processing architecture in terms of response time are included. The proposed architecture is not limited to using relaxation matching based servers, but is also applicable to other types of matching server as well.

关键词： Trademarks Image retrieval Computer architecture Scalability Delay Character recognition Image databases Hardware Gray-scale Systems engineering and theory

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Noise-robust Apple Disease Classification with Image Augmentation Methods

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Transactions of the Korean Institute of Electrical Engineers 2022年第9期71卷 1302-1307页

作者： Kim, Jang-Yeon Kim, Tae-Kyeong Cho, Hyun-Chong Dept. of Electronics Engineering and Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of Dept. of Electronics Engineering Kangwon National University Korea Republic of Dept. of Interdisciplinary Graduate Program for BIT Medical Convergence Kangwon National University Korea Republic of

When the apple disease occurs, accurate and rapid control must be carried out. If appropriate measures are not taken, the spread of the disease and secondary damage such as soil contamination caused by pesticides may occur. In this paper, the apple disease classification system that can classify the type of disease as well as normal from image is proposed. The apple disease classes consists of Marssonina blotch, Fire Blight, Valsa cacker, Alernaria blotch, and Bitter rot. Xception network was used to extract and learn features from image. Google's AutoAugment CIFAR-10 policy is used to increase apple disease data to increase network's classification performance. Then, in order to increase the reliability of data, the augmented data was selected by model trained only with original data. Gaussian, Salt-and-pepper, Speckle and Poisson noise were added to the test data to show good performance for noisy input data. We compared the performance of the model trained with original data and augmented data selected by threshold value 0.9. As a result, the proposed study showed a performance improvement of up to 6% in F1-Score. © 2022 Korean Institute of Electrical Engineers. All rights reserved.

关键词： Deep learning

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