Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally.According to clinical statistics,one woman out of eight is under the threat of br...Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally.According to clinical statistics,one woman out of eight is under the threat of breast cancer.Lifestyle and inheritance patterns may be a reason behind its spread among women.However,some preventive measures,such as tests and periodic clinical checks can mitigate its risk thereby,improving its survival chances substantially.Early diagnosis and initial stage treatment can help increase the survival rate.For that purpose,pathologists can gather support from nondestructive and efficient computer-aided diagnosis(CAD)systems.This study explores the breast cancer CAD method relying on multimodal medical imaging and decision-based fusion.In multimodal medical imaging fusion,a deep learning approach is applied,obtaining 97.5%accuracy with a 2.5%miss rate for breast cancer prediction.A deep extreme learning machine technique applied on feature-based data provided a 97.41%accuracy.Finally,decisionbased fusion applied to both breast cancer prediction models to diagnose its stages,resulted in an overall accuracy of 97.97%.The proposed system model provides more accurate results compared with other state-of-the-art approaches,rapidly diagnosing breast cancer to decrease its mortality rate.展开更多
This study presents a boosted vehicle detection system. It first hypothesizes potential locations of vehicles to reduce the computational costs by a statistic of the edge intensity and symmetry, then verifies the accu...This study presents a boosted vehicle detection system. It first hypothesizes potential locations of vehicles to reduce the computational costs by a statistic of the edge intensity and symmetry, then verifies the accuracy of the hypotheses using AdaBoost and Probabilistic Decision-Based Neural Network (PDBNN) classifiers, which exploit local and global features of vehicles, respectively. The combination of 2 classifiers can be used to learn the complementary relationship between local and global features, and it gains an extremely low false positive rate while maintaining a high detection rate. For the MIT Center for Biological & Computational Learning (CBCL) database, a 96.3% detection rate leads to a false alarm rate of approximately 0.0013%. The objective of this study is to extract the characteristic of vehicles in both local- and global-orientation, and model the implicit invariance of vehicles. This boosted approach provides a more effective solution to handle the problems encountered by conventional background-based detection systems. The experimental results of this study prove that the proposed system achieves good performance in detecting vehicles without background information. The implemented system also extract useful traffic information that can be used for further processing, such as tracking, counting, classification, and recognition.展开更多
基金supported by the KIAS(Research No.CG076601)in part by Sejong University Faculty Research Fund.
文摘Breast cancer is the most frequently detected tumor that eventually could result in a significant increase in female mortality globally.According to clinical statistics,one woman out of eight is under the threat of breast cancer.Lifestyle and inheritance patterns may be a reason behind its spread among women.However,some preventive measures,such as tests and periodic clinical checks can mitigate its risk thereby,improving its survival chances substantially.Early diagnosis and initial stage treatment can help increase the survival rate.For that purpose,pathologists can gather support from nondestructive and efficient computer-aided diagnosis(CAD)systems.This study explores the breast cancer CAD method relying on multimodal medical imaging and decision-based fusion.In multimodal medical imaging fusion,a deep learning approach is applied,obtaining 97.5%accuracy with a 2.5%miss rate for breast cancer prediction.A deep extreme learning machine technique applied on feature-based data provided a 97.41%accuracy.Finally,decisionbased fusion applied to both breast cancer prediction models to diagnose its stages,resulted in an overall accuracy of 97.97%.The proposed system model provides more accurate results compared with other state-of-the-art approaches,rapidly diagnosing breast cancer to decrease its mortality rate.
文摘This study presents a boosted vehicle detection system. It first hypothesizes potential locations of vehicles to reduce the computational costs by a statistic of the edge intensity and symmetry, then verifies the accuracy of the hypotheses using AdaBoost and Probabilistic Decision-Based Neural Network (PDBNN) classifiers, which exploit local and global features of vehicles, respectively. The combination of 2 classifiers can be used to learn the complementary relationship between local and global features, and it gains an extremely low false positive rate while maintaining a high detection rate. For the MIT Center for Biological & Computational Learning (CBCL) database, a 96.3% detection rate leads to a false alarm rate of approximately 0.0013%. The objective of this study is to extract the characteristic of vehicles in both local- and global-orientation, and model the implicit invariance of vehicles. This boosted approach provides a more effective solution to handle the problems encountered by conventional background-based detection systems. The experimental results of this study prove that the proposed system achieves good performance in detecting vehicles without background information. The implemented system also extract useful traffic information that can be used for further processing, such as tracking, counting, classification, and recognition.
