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- Keyword
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- Arrhythmia classication (1)
- Binary semantic segmentation (1)
- CNN (1)
- Computer vision (1)
- Convolutional neural networks (1)
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- Critical realism (1)
- Deep learning (1)
- Electrocardiogram (1)
- Ethnography (1)
- Image preprocessing (1)
- Image segmentation (1)
- Methods in qualitative inquiry (1)
- Observational research (1)
- One-dimensional convolutional neural network (1D-CNN) (1)
- PA (1)
- Priority model integrated voting method (1)
- Prostate cancer (1)
- Qualitative evaluation (1)
- Three-heartbeat multi-lead (THML) (1)
- Tooth disease recognition (1)
Articles 1 - 4 of 4
Full-Text Articles in Medicine and Health Sciences
A High-Accuracy Detection System: Based On Transfer Learning For Apical Lesions On Periapical Radiograph, Yueh Chuo, Wen-Ming Lin, Tsung-Yi Chen, Mei-Ling Chan, Yu-Sung Chang, Yan-Ru Lin, Yuan-Jin Lin, Yu-Han Shao, Chiung-An Chen, Patricia Angela R. Abu
A High-Accuracy Detection System: Based On Transfer Learning For Apical Lesions On Periapical Radiograph, Yueh Chuo, Wen-Ming Lin, Tsung-Yi Chen, Mei-Ling Chan, Yu-Sung Chang, Yan-Ru Lin, Yuan-Jin Lin, Yu-Han Shao, Chiung-An Chen, Patricia Angela R. Abu
Department of Information Systems & Computer Science Faculty Publications
Apical Lesions, one of the most common oral diseases, can be effectively detected in daily dental examinations by a periapical radiograph (PA). In the current popular endodontic treatment, most dentists spend a lot of time manually marking the lesion area. In order to reduce the burden on dentists, this paper proposes a convolutional neural network (CNN)-based regional analysis model for spical lesions for periapical radiographs. In this study, the database was provided by dentists with more than three years of practical experience, meeting the criteria for clinical practical application. The contributions of this work are (1) an advanced adaptive threshold …
Three-Heartbeat Multilead Ecg Recognition Method For Arrhythmia Classification, Liang-Hung Wang, Yan-Ting Yu, Wei Liu, Lu Xu, Chao-Xin Xie, Tao Yang, I-Chun Kuo, Xin-Kang Wang, Jie Gao, Patricia Angela R. Abu
Three-Heartbeat Multilead Ecg Recognition Method For Arrhythmia Classification, Liang-Hung Wang, Yan-Ting Yu, Wei Liu, Lu Xu, Chao-Xin Xie, Tao Yang, I-Chun Kuo, Xin-Kang Wang, Jie Gao, Patricia Angela R. Abu
Department of Information Systems & Computer Science Faculty Publications
Electrocardiogram (ECG) is the primary basis for the diagnosis of cardiovascular diseases. However, the amount of ECG data of patients makes manual interpretation time-consuming and onerous. Therefore, the intelligent ECG recognition technology is an important means to decrease the shortage of medical resources. This study proposes a novel classification method for arrhythmia that uses for the very first time a three-heartbeat multi-lead (THML) ECG data in which each fragment contains three complete heartbeat processes of multiple ECG leads. The THML ECG data pre-processing method is formulated which makes use of the MIT-BIH arrhythmia database as training samples. Four arrhythmia classification …
Participant Use Of Digital Diaries In Qualitative Research: A Strong Structuration Analysis, Maureen L. Seguin, Jhaki A. Mendoza, Eunice Mallari, Gideon Lasco, Arianna Maever L. Amit, Lia M. Palileo-Villanueva, Benjamin Palafox, Alicia Renedo, Martin Mckee, Dina Balabanova
Participant Use Of Digital Diaries In Qualitative Research: A Strong Structuration Analysis, Maureen L. Seguin, Jhaki A. Mendoza, Eunice Mallari, Gideon Lasco, Arianna Maever L. Amit, Lia M. Palileo-Villanueva, Benjamin Palafox, Alicia Renedo, Martin Mckee, Dina Balabanova
Development Studies Faculty Publications
Lived experiences of chronic conditions are marked by fluidity and change, which should be considered if care is to be truly patient-centered. Seeking to capture this fluidity, we used digital diaries via mobile phones to communicate with hypertensive patients in the Philippines over approximately 12 months. This paper draws on Strong Structuration Theory to conceptualize the complex array of factors shaping participants’ usage (and non-usage) of the diaries, thereby offering a comprehensive understanding of how the diaries were perceived and used. We draw on qualitative data from 42 participants (73 baseline/follow-up interviews and 37 digital diaries), purposively selected from a …
Construction Of A Repeatable Framework For Prostate Cancer Lesion Binary Semantic Segmentation Using Convolutional Neural Networks, Ian Vincent O. Mirasol, Patricia Angela R. Abu, Rosula Sj Reyes
Construction Of A Repeatable Framework For Prostate Cancer Lesion Binary Semantic Segmentation Using Convolutional Neural Networks, Ian Vincent O. Mirasol, Patricia Angela R. Abu, Rosula Sj Reyes
Department of Information Systems & Computer Science Faculty Publications
Prostate cancer is the 3rd most diagnosed cancer overall. Current screening methods such as the prostate-specific antigen test could result in overdiagonosis and overtreatment while other methods such as a transrectal ultrasonography are invasive. Recent medical advancements have allowed the use of multiparametric MRI — a noninvasive and reliable screening process for prostate cancer. However, assessment would still vary from different professionals introducing subjectivity. While con-volutional neural network has been used in multiple studies to ob-jectively segment prostate lesions, due to the sensitivity of datasets and varying ground-truth established used in these studies, it is not possible to reproduce and …