Author
Sang-Woong Lee
Other affiliations: Carnegie Mellon University, Chosun University, Korea University
Bio: Sang-Woong Lee is an academic researcher from Gachon University. The author has contributed to research in topics: Facial recognition system & Medicine. The author has an hindex of 17, co-authored 80 publications receiving 912 citations. Previous affiliations of Sang-Woong Lee include Carnegie Mellon University & Chosun University.
Papers published on a yearly basis
Papers
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TL;DR: This study proposes an approach that utilizes deep learning models with convolutional layers to extract the most useful visual features for breast cancer classification and demonstrates that this deep learning model significantly outperforms state-of-the-art methods.
140 citations
TL;DR: Experiments showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects and compare the performance of these classifiers for volumetric sMR image data from Alzheimer's disease neuroimaging initiative (ADNI) datasets.
Abstract: Alzheimer’s disease (AD) is a progressive, neurodegenerative brain disorder that attacks neurotransmitters, brain cells, and nerves, affecting brain functions, memory, and behaviors and then finally causing dementia on elderly people. Despite its significance, there is currently no cure for it. However, there are medicines available on prescription that can help delay the progress of the condition. Thus, early diagnosis of AD is essential for patient care and relevant researches. Major challenges in proper diagnosis of AD using existing classification schemes are the availability of a smaller number of training samples and the larger number of possible feature representations. In this paper, we present and compare AD diagnosis approaches using structural magnetic resonance (sMR) images to discriminate AD, mild cognitive impairment (MCI), and healthy control (HC) subjects using a support vector machine (SVM), an import vector machine (IVM), and a regularized extreme learning machine (RELM). The greedy score-based feature selection technique is employed to select important feature vectors. In addition, a kernel-based discriminative approach is adopted to deal with complex data distributions. We compare the performance of these classifiers for volumetric sMR image data from Alzheimer’s disease neuroimaging initiative (ADNI) datasets. Experiments on the ADNI datasets showed that RELM with the feature selection approach can significantly improve classification accuracy of AD from MCI and HC subjects.
94 citations
TL;DR: The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019.
94 citations
94 citations
TL;DR: A new method of extending the SVDD, which is one of the most well-known support vector learning methods for the one-class problem, is proposed, which can recognize a person even with a low-resolution image.
91 citations
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TL;DR: This survey focuses on approaches that aim on classification of full-body motions, such as kicking, punching, and waving, and categorizes them according to how they represent the spatial and temporal structure of actions.
1,058 citations
TL;DR: A survey of semantic segmentation methods by categorizing them into ten different classes according to the common concepts underlying their architectures, and providing an overview of the publicly available datasets on which they have been assessed.
371 citations