Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status...Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.展开更多
The precise identification of date palm tree diseases is essential for maintaining agricultural productivity and promoting sustainable farming methods.Conventional approaches rely on visual examination by experts to d...The precise identification of date palm tree diseases is essential for maintaining agricultural productivity and promoting sustainable farming methods.Conventional approaches rely on visual examination by experts to detect infected palm leaves,which is time intensive and susceptible to mistakes.This study proposes an automated leaf classification system that uses deep learning algorithms to identify and categorize diseases in date palm tree leaves with high precision and dependability.The system leverages pretrained convolutional neural network architectures(InceptionV3,DenseNet,and MobileNet)to extract and examine leaf characteristics for classification purposes.A publicly accessible dataset comprising multiple classes of diseased and healthy date palm leaf samples was used for the training and assessment.Data augmentation techniques were implemented to enhance the dataset and improve model resilience.In addition,Synthetic Minority Oversampling Technique(SMOTE)was applied to address class imbalance and further improve the classification performance.The system was trained and evaluated using this dataset,and two of the models,DenseNet and MobileNet,achieved classification accuracies greater than 95%.MobileNetV2 emerged as the top-performing model among those assessed,achieving an overall accuracy of 96.99%and macro-average F1-score of 0.97.All nine categories of date palm leaf conditions were consistently and accurately identified,showing exceptional precision and dependability.Comparative experiments were conducted to assess the performance of the Convolutional Neural Network(CNN)architectures and demonstrate their potential for scalable and automated disease detection.This system has the potential to serve as a valuable agricultural tool for assisting in disease management and monitoring date palm cultivation.展开更多
基金supported by the Deanship of Research and Graduate Studies at King Khalid University under Small Research Project grant number RGP1/139/45.
文摘Integrating multiple medical imaging techniques,including Magnetic Resonance Imaging(MRI),Computed Tomography,Positron Emission Tomography(PET),and ultrasound,provides a comprehensive view of the patient health status.Each of these methods contributes unique diagnostic insights,enhancing the overall assessment of patient condition.Nevertheless,the amalgamation of data from multiple modalities presents difficulties due to disparities in resolution,data collection methods,and noise levels.While traditional models like Convolutional Neural Networks(CNNs)excel in single-modality tasks,they struggle to handle multi-modal complexities,lacking the capacity to model global relationships.This research presents a novel approach for examining multi-modal medical imagery using a transformer-based system.The framework employs self-attention and cross-attention mechanisms to synchronize and integrate features across various modalities.Additionally,it shows resilience to variations in noise and image quality,making it adaptable for real-time clinical use.To address the computational hurdles linked to transformer models,particularly in real-time clinical applications in resource-constrained environments,several optimization techniques have been integrated to boost scalability and efficiency.Initially,a streamlined transformer architecture was adopted to minimize the computational load while maintaining model effectiveness.Methods such as model pruning,quantization,and knowledge distillation have been applied to reduce the parameter count and enhance the inference speed.Furthermore,efficient attention mechanisms such as linear or sparse attention were employed to alleviate the substantial memory and processing requirements of traditional self-attention operations.For further deployment optimization,researchers have implemented hardware-aware acceleration strategies,including the use of TensorRT and ONNX-based model compression,to ensure efficient execution on edge devices.These optimizations allow the approach to function effectively in real-time clinical settings,ensuring viability even in environments with limited resources.Future research directions include integrating non-imaging data to facilitate personalized treatment and enhancing computational efficiency for implementation in resource-limited environments.This study highlights the transformative potential of transformer models in multi-modal medical imaging,offering improvements in diagnostic accuracy and patient care outcomes.
基金funded by the Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R821),Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The precise identification of date palm tree diseases is essential for maintaining agricultural productivity and promoting sustainable farming methods.Conventional approaches rely on visual examination by experts to detect infected palm leaves,which is time intensive and susceptible to mistakes.This study proposes an automated leaf classification system that uses deep learning algorithms to identify and categorize diseases in date palm tree leaves with high precision and dependability.The system leverages pretrained convolutional neural network architectures(InceptionV3,DenseNet,and MobileNet)to extract and examine leaf characteristics for classification purposes.A publicly accessible dataset comprising multiple classes of diseased and healthy date palm leaf samples was used for the training and assessment.Data augmentation techniques were implemented to enhance the dataset and improve model resilience.In addition,Synthetic Minority Oversampling Technique(SMOTE)was applied to address class imbalance and further improve the classification performance.The system was trained and evaluated using this dataset,and two of the models,DenseNet and MobileNet,achieved classification accuracies greater than 95%.MobileNetV2 emerged as the top-performing model among those assessed,achieving an overall accuracy of 96.99%and macro-average F1-score of 0.97.All nine categories of date palm leaf conditions were consistently and accurately identified,showing exceptional precision and dependability.Comparative experiments were conducted to assess the performance of the Convolutional Neural Network(CNN)architectures and demonstrate their potential for scalable and automated disease detection.This system has the potential to serve as a valuable agricultural tool for assisting in disease management and monitoring date palm cultivation.
