Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)rays.If left untreated,these di...Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)rays.If left untreated,these diseases can have severe consequences and spread,especially among children.Early detection is crucial to prevent their spread and improve a patient’s chances of recovery.Dermatology,the branch of medicine dealing with skin diseases,faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance,type of skin,and others.This study presents a method for detecting skin diseases using Deep Learning(DL),focusing on the most common diseases affecting children in Saudi Arabia due to the high UV value in most of the year,especially in the summer.The method utilizes various Convolutional Neural Network(CNN)architectures to classify skin conditions such as eczema,psoriasis,and ringworm.The proposed method demonstrates high accuracy rates of 99.99%and 97%using famous and effective transfer learning models MobileNet and DenseNet121,respectively.This illustrates the potential of DL in automating the detection of skin diseases and offers a promising approach for early diagnosis and treatment.展开更多
Handwritten character recognition(HCR)involves identifying characters in images,documents,and various sources such as forms surveys,questionnaires,and signatures,and transforming them into a machine-readable format fo...Handwritten character recognition(HCR)involves identifying characters in images,documents,and various sources such as forms surveys,questionnaires,and signatures,and transforming them into a machine-readable format for subsequent processing.Successfully recognizing complex and intricately shaped handwritten characters remains a significant obstacle.The use of convolutional neural network(CNN)in recent developments has notably advanced HCR,leveraging the ability to extract discriminative features from extensive sets of raw data.Because of the absence of pre-existing datasets in the Kurdish language,we created a Kurdish handwritten dataset called(KurdSet).The dataset consists of Kurdish characters,digits,texts,and symbols.The dataset consists of 1560 participants and contains 45,240 characters.In this study,we chose characters only from our dataset.We utilized a Kurdish dataset for handwritten character recognition.The study also utilizes various models,including InceptionV3,Xception,DenseNet121,and a customCNNmodel.To show the performance of the KurdSet dataset,we compared it to Arabic handwritten character recognition dataset(AHCD).We applied the models to both datasets to show the performance of our dataset.Additionally,the performance of the models is evaluated using test accuracy,which measures the percentage of correctly classified characters in the evaluation phase.All models performed well in the training phase,DenseNet121 exhibited the highest accuracy among the models,achieving a high accuracy of 99.80%on the Kurdish dataset.And Xception model achieved 98.66%using the Arabic dataset.展开更多
该研究针对当前自然环境下的苹果叶片病害识别中病害病斑小、空间分布特征不同以及特征相近病害识别困难的问题,设计DEFL (DenseNet121+EfficientNet with focal loss and label smoothing)模型。首先,该模型以并行的EfficientNet-B0网...该研究针对当前自然环境下的苹果叶片病害识别中病害病斑小、空间分布特征不同以及特征相近病害识别困难的问题,设计DEFL (DenseNet121+EfficientNet with focal loss and label smoothing)模型。首先,该模型以并行的EfficientNet-B0网络和DenseNet121网络为特征提取网络,以提升模型特征提取能力,其次引入结合标签平滑策略的焦点损失函数以加强模型对识别困难样本的关注。经测试,所提模型的识别准确率为99.13%,平均精度均值为98.47%。消融试验表明两项改进分别使模型平均精度均值提高了7.99和3.15个百分点。对比试验结果表明,DEFL模型平均精度均值较于ResNet50、Inception V3、ResNeXt模型以及分别融合这3种模型的EfficientNet-B0模型分别高出14.53、13.17、14.61、 6.4、 7.71以及8.91个百分点,模型规模分别小18.73、 7.7、 12.2、 83.62、 69.6以及60.09MB。Grad-CAM(gradient-weighted class activation mapping)热力图可视化结果表明所提模型重点关注了叶片病变区域。UMAP(uniform manifold approximation and projection)特征降维可视化结果表明所提模型提取的特征更具区分度。实际应用验证取得了97.73%的总体准确率以及95.82%的平均精度均值。综上,该研究提出的DEFL模型能够为苹果病害防治提供有效参考。展开更多
Turmeric Leaf diseases pose a major threat to turmeric cultivation,causing significant yield loss and economic impact.Early and accurate identification of these diseases is essential for effective crop management and ...Turmeric Leaf diseases pose a major threat to turmeric cultivation,causing significant yield loss and economic impact.Early and accurate identification of these diseases is essential for effective crop management and timely intervention.This study proposes DenseSwinGNNNet,a hybrid deep learning framework that integrates DenseNet-121,the Swin Transformer,and a Graph Neural Network(GNN)to enhance the classification of turmeric leaf conditions.DenseNet121 extracts discriminative low-level features,the Swin Transformer captures long-range contextual relationships through hierarchical self-attention,and the GNN models inter-feature dependencies to refine the final representation.A total of 4361 images from the Mendeley turmeric leaf dataset were used,categorized into four classes:Aphids Disease,Blotch,Leaf Spot,and Healthy Leaf.The dataset underwent extensive preprocessing,including augmentation,normalization,and resizing,to improve generalization.An 80:10:10 split was applied for training,validation,and testing respectively.Model performance was evaluated using accuracy,precision,recall,F1-score,confusion matrices,and ROC curves.Optimized with the Adam optimizer at the learning rate of 0.0001,DenseSwinGNNNet achieved an overall accuracy of 99.7%,with precision,recall,and F1-scores exceeding 99%across all classes.The ROC curves reported AUC values near 1.0,indicating excellent class separability,while the confusion matrix showed minimal misclassification.Beyond high predictive performance,the framework incorporates considerations for cybersecurity and privacy in data-driven agriculture,supporting secure data handling and robust model deployment.This work contributes a reliable and scalable approach for turmeric leaf disease detection and advances the application of AI-driven precision agriculture.展开更多
文摘Human beings are often affected by a wide range of skin diseases,which can be attributed to genetic factors and environmental influences,such as exposure to sunshine with ultraviolet(UV)rays.If left untreated,these diseases can have severe consequences and spread,especially among children.Early detection is crucial to prevent their spread and improve a patient’s chances of recovery.Dermatology,the branch of medicine dealing with skin diseases,faces challenges in accurately diagnosing these conditions due to the difficulty in identifying and distinguishing between different diseases based on their appearance,type of skin,and others.This study presents a method for detecting skin diseases using Deep Learning(DL),focusing on the most common diseases affecting children in Saudi Arabia due to the high UV value in most of the year,especially in the summer.The method utilizes various Convolutional Neural Network(CNN)architectures to classify skin conditions such as eczema,psoriasis,and ringworm.The proposed method demonstrates high accuracy rates of 99.99%and 97%using famous and effective transfer learning models MobileNet and DenseNet121,respectively.This illustrates the potential of DL in automating the detection of skin diseases and offers a promising approach for early diagnosis and treatment.
文摘Handwritten character recognition(HCR)involves identifying characters in images,documents,and various sources such as forms surveys,questionnaires,and signatures,and transforming them into a machine-readable format for subsequent processing.Successfully recognizing complex and intricately shaped handwritten characters remains a significant obstacle.The use of convolutional neural network(CNN)in recent developments has notably advanced HCR,leveraging the ability to extract discriminative features from extensive sets of raw data.Because of the absence of pre-existing datasets in the Kurdish language,we created a Kurdish handwritten dataset called(KurdSet).The dataset consists of Kurdish characters,digits,texts,and symbols.The dataset consists of 1560 participants and contains 45,240 characters.In this study,we chose characters only from our dataset.We utilized a Kurdish dataset for handwritten character recognition.The study also utilizes various models,including InceptionV3,Xception,DenseNet121,and a customCNNmodel.To show the performance of the KurdSet dataset,we compared it to Arabic handwritten character recognition dataset(AHCD).We applied the models to both datasets to show the performance of our dataset.Additionally,the performance of the models is evaluated using test accuracy,which measures the percentage of correctly classified characters in the evaluation phase.All models performed well in the training phase,DenseNet121 exhibited the highest accuracy among the models,achieving a high accuracy of 99.80%on the Kurdish dataset.And Xception model achieved 98.66%using the Arabic dataset.
文摘该研究针对当前自然环境下的苹果叶片病害识别中病害病斑小、空间分布特征不同以及特征相近病害识别困难的问题,设计DEFL (DenseNet121+EfficientNet with focal loss and label smoothing)模型。首先,该模型以并行的EfficientNet-B0网络和DenseNet121网络为特征提取网络,以提升模型特征提取能力,其次引入结合标签平滑策略的焦点损失函数以加强模型对识别困难样本的关注。经测试,所提模型的识别准确率为99.13%,平均精度均值为98.47%。消融试验表明两项改进分别使模型平均精度均值提高了7.99和3.15个百分点。对比试验结果表明,DEFL模型平均精度均值较于ResNet50、Inception V3、ResNeXt模型以及分别融合这3种模型的EfficientNet-B0模型分别高出14.53、13.17、14.61、 6.4、 7.71以及8.91个百分点,模型规模分别小18.73、 7.7、 12.2、 83.62、 69.6以及60.09MB。Grad-CAM(gradient-weighted class activation mapping)热力图可视化结果表明所提模型重点关注了叶片病变区域。UMAP(uniform manifold approximation and projection)特征降维可视化结果表明所提模型提取的特征更具区分度。实际应用验证取得了97.73%的总体准确率以及95.82%的平均精度均值。综上,该研究提出的DEFL模型能够为苹果病害防治提供有效参考。
基金supported through the Ongoing Research Funding Program(ORF-2025-498)King Saud University,Riyadh,Saudi Arabia。
文摘Turmeric Leaf diseases pose a major threat to turmeric cultivation,causing significant yield loss and economic impact.Early and accurate identification of these diseases is essential for effective crop management and timely intervention.This study proposes DenseSwinGNNNet,a hybrid deep learning framework that integrates DenseNet-121,the Swin Transformer,and a Graph Neural Network(GNN)to enhance the classification of turmeric leaf conditions.DenseNet121 extracts discriminative low-level features,the Swin Transformer captures long-range contextual relationships through hierarchical self-attention,and the GNN models inter-feature dependencies to refine the final representation.A total of 4361 images from the Mendeley turmeric leaf dataset were used,categorized into four classes:Aphids Disease,Blotch,Leaf Spot,and Healthy Leaf.The dataset underwent extensive preprocessing,including augmentation,normalization,and resizing,to improve generalization.An 80:10:10 split was applied for training,validation,and testing respectively.Model performance was evaluated using accuracy,precision,recall,F1-score,confusion matrices,and ROC curves.Optimized with the Adam optimizer at the learning rate of 0.0001,DenseSwinGNNNet achieved an overall accuracy of 99.7%,with precision,recall,and F1-scores exceeding 99%across all classes.The ROC curves reported AUC values near 1.0,indicating excellent class separability,while the confusion matrix showed minimal misclassification.Beyond high predictive performance,the framework incorporates considerations for cybersecurity and privacy in data-driven agriculture,supporting secure data handling and robust model deployment.This work contributes a reliable and scalable approach for turmeric leaf disease detection and advances the application of AI-driven precision agriculture.
