In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.De...In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.展开更多
Cyber-physical systems(CPS)are increasingly commonplace,with applications in energy,health,transportation,and many other sectors.One of the major requirements in CPS is that the interaction between cyber-world and man...Cyber-physical systems(CPS)are increasingly commonplace,with applications in energy,health,transportation,and many other sectors.One of the major requirements in CPS is that the interaction between cyber-world and man-made physical world(exchanging and sharing of data and information with other physical objects and systems)must be safe,especially in bi-directional communications.In particular,there is a need to suitably address security and/or privacy concerns in this human-in-the-loop CPS ecosystem.However,existing centralized architecture models in CPS,and also the more general IoT systems,have a number of associated limitations,in terms of single point of failure,data privacy,security,robustness,etc.Such limitations reinforce the importance of designing reliable,secure and privacy-preserving distributed solutions and other novel approaches,such as those based on blockchain technology due to its features(e.g.,decentralization,transparency and immutability of data).This is the focus of this special issue.展开更多
基金the Deanship for Research Innovation,Ministry of Education in Saudi Arabia,for funding this research work through project number IFKSUDR-H122.
文摘In the current landscape of the COVID-19 pandemic,the utilization of deep learning in medical imaging,especially in chest computed tomography(CT)scan analysis for virus detection,has become increasingly significant.Despite its potential,deep learning’s“black box”nature has been a major impediment to its broader acceptance in clinical environments,where transparency in decision-making is imperative.To bridge this gap,our research integrates Explainable AI(XAI)techniques,specifically the Local Interpretable Model-Agnostic Explanations(LIME)method,with advanced deep learning models.This integration forms a sophisticated and transparent framework for COVID-19 identification,enhancing the capability of standard Convolutional Neural Network(CNN)models through transfer learning and data augmentation.Our approach leverages the refined DenseNet201 architecture for superior feature extraction and employs data augmentation strategies to foster robust model generalization.The pivotal element of our methodology is the use of LIME,which demystifies the AI decision-making process,providing clinicians with clear,interpretable insights into the AI’s reasoning.This unique combination of an optimized Deep Neural Network(DNN)with LIME not only elevates the precision in detecting COVID-19 cases but also equips healthcare professionals with a deeper understanding of the diagnostic process.Our method,validated on the SARS-COV-2 CT-Scan dataset,demonstrates exceptional diagnostic accuracy,with performance metrics that reinforce its potential for seamless integration into modern healthcare systems.This innovative approach marks a significant advancement in creating explainable and trustworthy AI tools for medical decisionmaking in the ongoing battle against COVID-19.
文摘Cyber-physical systems(CPS)are increasingly commonplace,with applications in energy,health,transportation,and many other sectors.One of the major requirements in CPS is that the interaction between cyber-world and man-made physical world(exchanging and sharing of data and information with other physical objects and systems)must be safe,especially in bi-directional communications.In particular,there is a need to suitably address security and/or privacy concerns in this human-in-the-loop CPS ecosystem.However,existing centralized architecture models in CPS,and also the more general IoT systems,have a number of associated limitations,in terms of single point of failure,data privacy,security,robustness,etc.Such limitations reinforce the importance of designing reliable,secure and privacy-preserving distributed solutions and other novel approaches,such as those based on blockchain technology due to its features(e.g.,decentralization,transparency and immutability of data).This is the focus of this special issue.
