随着深度学习技术的日益精进,它在植物病害识别领域的应用研究日趋深入,而优化AlexNet模型能有效提升桑叶病害识别的性能。因此,选用AlexNet作为基础网络,针对AlexNet的主干网络和多尺度特征融合策略进行改进,并提出一个新型的网络架构...随着深度学习技术的日益精进,它在植物病害识别领域的应用研究日趋深入,而优化AlexNet模型能有效提升桑叶病害识别的性能。因此,选用AlexNet作为基础网络,针对AlexNet的主干网络和多尺度特征融合策略进行改进,并提出一个新型的网络架构——IP-AlexNet模型。首先,在卷积层之后,引入Inception模块,以捕获桑叶病害图像的多样化特征,并通过减少卷积核降低网络计算的复杂度;其次,利用金字塔卷积进行多尺度特征融合,以增强模型的准确性和鲁棒性;再次,加入SE(Squeeze and Excitation)注意力机制,使模型能够聚焦于图像中的关键区域或特征,从而提高识别的精确度和效率;最后,使用自适应平均池化替换传统的最大池化以生成更平滑的特征图,从而减少图像特征信息的损失。实验结果表明,IP-AlexNet模型在桑叶病害识别方面取得了较好的效果,识别准确率高达95.33%,较AlexNet模型提升了9.66个百分点。另外,精准率、召回率、F1值和混淆矩阵等多元评价指标的综合分析表明,IP-AlexNet模型具有很好的鲁棒性和稳定性。展开更多
In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue...In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.展开更多
Background:A major side effect of diabetes is diabetic retinopathy(DR),which can cause irreparable blindness if left untreated.Because of the additional psychological and social strains,controlling comorbidities like ...Background:A major side effect of diabetes is diabetic retinopathy(DR),which can cause irreparable blindness if left untreated.Because of the additional psychological and social strains,controlling comorbidities like DR becomes crucial for cancer patients,particularly those receiving treatments like chemotherapy.Both the patient and their caretakers may have severe effects from vision impairment,including increased anxiety,depression,and a lower quality of life.One can reduce these psychological pressures by facilitating prompt intervention,early identification,and categorization of DR.Methods:This work uses a metaheuristic optimization technique to offer a sophisticated,automated categorization system for DR.The system combines Attention AlexNet with an Improved Nutcracker Optimizer,which optimizes the weights and hyperparameters of deep learning models to improve classification accuracy.Results:The approach achieves high classification accuracy of 99.43%and enhanced precision and recall when tested on two popular image datasets,APTOS-2019 and EyePacs.Conclusions:By addressing the technological improvement in DR detection,this work contributes to the multidisciplinary approach of psycho-oncology and helps lessen the psychological distress that cancer patients experience when they lose their eyesight.Ultimately,it supports the general well-being and mental health of people facing diabetes-related problems and cancer by highlighting the significance of incorporating cutting-edge machine learning technologies into clinical practice.展开更多
文摘随着深度学习技术的日益精进,它在植物病害识别领域的应用研究日趋深入,而优化AlexNet模型能有效提升桑叶病害识别的性能。因此,选用AlexNet作为基础网络,针对AlexNet的主干网络和多尺度特征融合策略进行改进,并提出一个新型的网络架构——IP-AlexNet模型。首先,在卷积层之后,引入Inception模块,以捕获桑叶病害图像的多样化特征,并通过减少卷积核降低网络计算的复杂度;其次,利用金字塔卷积进行多尺度特征融合,以增强模型的准确性和鲁棒性;再次,加入SE(Squeeze and Excitation)注意力机制,使模型能够聚焦于图像中的关键区域或特征,从而提高识别的精确度和效率;最后,使用自适应平均池化替换传统的最大池化以生成更平滑的特征图,从而减少图像特征信息的损失。实验结果表明,IP-AlexNet模型在桑叶病害识别方面取得了较好的效果,识别准确率高达95.33%,较AlexNet模型提升了9.66个百分点。另外,精准率、召回率、F1值和混淆矩阵等多元评价指标的综合分析表明,IP-AlexNet模型具有很好的鲁棒性和稳定性。
文摘In radiology,magnetic resonance imaging(MRI)is an essential diagnostic tool that provides detailed images of a patient’s anatomical and physiological structures.MRI is particularly effective for detecting soft tissue anomalies.Traditionally,radiologists manually interpret these images,which can be labor-intensive and time-consuming due to the vast amount of data.To address this challenge,machine learning,and deep learning approaches can be utilized to improve the accuracy and efficiency of anomaly detection in MRI scans.This manuscript presents the use of the Deep AlexNet50 model for MRI classification with discriminative learning methods.There are three stages for learning;in the first stage,the whole dataset is used to learn the features.In the second stage,some layers of AlexNet50 are frozen with an augmented dataset,and in the third stage,AlexNet50 with an augmented dataset with the augmented dataset.This method used three publicly available MRI classification datasets:Harvard whole brain atlas(HWBA-dataset),the School of Biomedical Engineering of Southern Medical University(SMU-dataset),and The National Institute of Neuroscience and Hospitals brain MRI dataset(NINS-dataset)for analysis.Various hyperparameter optimizers like Adam,stochastic gradient descent(SGD),Root mean square propagation(RMS prop),Adamax,and AdamW have been used to compare the performance of the learning process.HWBA-dataset registers maximum classification performance.We evaluated the performance of the proposed classification model using several quantitative metrics,achieving an average accuracy of 98%.
文摘Background:A major side effect of diabetes is diabetic retinopathy(DR),which can cause irreparable blindness if left untreated.Because of the additional psychological and social strains,controlling comorbidities like DR becomes crucial for cancer patients,particularly those receiving treatments like chemotherapy.Both the patient and their caretakers may have severe effects from vision impairment,including increased anxiety,depression,and a lower quality of life.One can reduce these psychological pressures by facilitating prompt intervention,early identification,and categorization of DR.Methods:This work uses a metaheuristic optimization technique to offer a sophisticated,automated categorization system for DR.The system combines Attention AlexNet with an Improved Nutcracker Optimizer,which optimizes the weights and hyperparameters of deep learning models to improve classification accuracy.Results:The approach achieves high classification accuracy of 99.43%and enhanced precision and recall when tested on two popular image datasets,APTOS-2019 and EyePacs.Conclusions:By addressing the technological improvement in DR detection,this work contributes to the multidisciplinary approach of psycho-oncology and helps lessen the psychological distress that cancer patients experience when they lose their eyesight.Ultimately,it supports the general well-being and mental health of people facing diabetes-related problems and cancer by highlighting the significance of incorporating cutting-edge machine learning technologies into clinical practice.
