Diabetic Retinopathy(DR)is a critical disorder that affects the retina due to the constant rise in diabetics and remains the major cause of blindness across the world.Early detection and timely treatment are essential...Diabetic Retinopathy(DR)is a critical disorder that affects the retina due to the constant rise in diabetics and remains the major cause of blindness across the world.Early detection and timely treatment are essential to mitigate the effects of DR,such as retinal damage and vision impairment.Several conventional approaches have been proposed to detect DR early and accurately,but they are limited by data imbalance,interpretability,overfitting,convergence time,and other issues.To address these drawbacks and improve DR detection accurately,a distributed Explainable Convolutional Neural network-enabled Light Gradient Boosting Machine(DE-ExLNN)is proposed in this research.The model combines an explainable Convolutional Neural Network(CNN)and Light Gradient Boosting Machine(LightGBM),achieving highly accurate outcomes in DR detection.LightGBM serves as the detection model,and the inclusion of an explainable CNN addresses issues that conventional CNN classifiers could not resolve.A custom dataset was created for this research,containing both fundus and OCTA images collected from a realtime environment,providing more accurate results compared to standard conventional DR datasets.The custom dataset demonstrates notable accuracy,sensitivity,specificity,and Matthews Correlation Coefficient(MCC)scores,underscoring the effectiveness of this approach.Evaluations against other standard datasets achieved an accuracy of 93.94%,sensitivity of 93.90%,specificity of 93.99%,and MCC of 93.88%for fundus images.For OCTA images,the results obtained an accuracy of 95.30%,sensitivity of 95.50%,specificity of 95.09%,andMCC of 95%.Results prove that the combination of explainable CNN and LightGBMoutperforms othermethods.The inclusion of distributed learning enhances the model’s efficiency by reducing time consumption and complexity while facilitating feature extraction.展开更多
The requirements for ensuring functional safety have always been very high.Modern safety-related systems are becoming increasingly complex, making also the safety integrity assessment more complex and time-consuming. ...The requirements for ensuring functional safety have always been very high.Modern safety-related systems are becoming increasingly complex, making also the safety integrity assessment more complex and time-consuming. This trend is further intensified by the fact that AI-based algorithms are finding their way into safety-related systems or will do so in the future. However, existing and expected standards and regulations for the use of AI methods pose significant challenges for the development of embedded AI software in functional safety-related systems. The consideration of essential requirements from various perspectives necessitates an intensive examination of the subject matter, especially as diferent standards have to be taken into account depending on the final application. There are also diferent targets for the “safe behavior” of a system depending on the target application. While stopping all movements of a machine in industrial production plants is likely to be considered a “safe state”, the same condition might not be considered as safe in flying aircraft, driving cars or medicine equipment like heart pacemaker. This overall complexity is operationalized in our approach in such a way that it is straightforward to monitor conformity with the requirements. To support safety integrity assessments and reduce the required efort, a Self-Enforcing Network(SEN) model is presented in which developers or safety experts can indicate the degree of fulfillment of certain requirements with possible impact on the safety integrity of a safety-related system. The result evaluated by the SEN model indicates the achievable safety integrity level of the assessed system, which is additionally provided by an explanatory component.展开更多
基金funded by the Centre for Advanced Modelling and Geospatial Information Systems(CAMGIS),Faculty of Engineering and IT,University of Technology Sydneysupported by the Research Funding Program,King Saud University,Riyadh,Saudi Arabia,under Project Ongoing Research Funding program(ORF-2025-14).
文摘Diabetic Retinopathy(DR)is a critical disorder that affects the retina due to the constant rise in diabetics and remains the major cause of blindness across the world.Early detection and timely treatment are essential to mitigate the effects of DR,such as retinal damage and vision impairment.Several conventional approaches have been proposed to detect DR early and accurately,but they are limited by data imbalance,interpretability,overfitting,convergence time,and other issues.To address these drawbacks and improve DR detection accurately,a distributed Explainable Convolutional Neural network-enabled Light Gradient Boosting Machine(DE-ExLNN)is proposed in this research.The model combines an explainable Convolutional Neural Network(CNN)and Light Gradient Boosting Machine(LightGBM),achieving highly accurate outcomes in DR detection.LightGBM serves as the detection model,and the inclusion of an explainable CNN addresses issues that conventional CNN classifiers could not resolve.A custom dataset was created for this research,containing both fundus and OCTA images collected from a realtime environment,providing more accurate results compared to standard conventional DR datasets.The custom dataset demonstrates notable accuracy,sensitivity,specificity,and Matthews Correlation Coefficient(MCC)scores,underscoring the effectiveness of this approach.Evaluations against other standard datasets achieved an accuracy of 93.94%,sensitivity of 93.90%,specificity of 93.99%,and MCC of 93.88%for fundus images.For OCTA images,the results obtained an accuracy of 95.30%,sensitivity of 95.50%,specificity of 95.09%,andMCC of 95%.Results prove that the combination of explainable CNN and LightGBMoutperforms othermethods.The inclusion of distributed learning enhances the model’s efficiency by reducing time consumption and complexity while facilitating feature extraction.
文摘The requirements for ensuring functional safety have always been very high.Modern safety-related systems are becoming increasingly complex, making also the safety integrity assessment more complex and time-consuming. This trend is further intensified by the fact that AI-based algorithms are finding their way into safety-related systems or will do so in the future. However, existing and expected standards and regulations for the use of AI methods pose significant challenges for the development of embedded AI software in functional safety-related systems. The consideration of essential requirements from various perspectives necessitates an intensive examination of the subject matter, especially as diferent standards have to be taken into account depending on the final application. There are also diferent targets for the “safe behavior” of a system depending on the target application. While stopping all movements of a machine in industrial production plants is likely to be considered a “safe state”, the same condition might not be considered as safe in flying aircraft, driving cars or medicine equipment like heart pacemaker. This overall complexity is operationalized in our approach in such a way that it is straightforward to monitor conformity with the requirements. To support safety integrity assessments and reduce the required efort, a Self-Enforcing Network(SEN) model is presented in which developers or safety experts can indicate the degree of fulfillment of certain requirements with possible impact on the safety integrity of a safety-related system. The result evaluated by the SEN model indicates the achievable safety integrity level of the assessed system, which is additionally provided by an explanatory component.
