Gliomas,the most prevalent primary brain tumors,require accurate segmentation for diagnosis and risk assess-ment.In this paper,we develop a novel deep learning-based method,the Dynamic Hierarchical Attention for Impro...Gliomas,the most prevalent primary brain tumors,require accurate segmentation for diagnosis and risk assess-ment.In this paper,we develop a novel deep learning-based method,the Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis(DHA-ISSP)model.The DHA-ISSP model combines a three-band 3D convolutional neural network(CNN)U-Net architecture with dynamic hierarchical attention mechanisms,enabling precise tumor segmentation and survival prediction.The DHA-ISSP model captures fine-grained details and contextual information by leveraging attention mechanisms at multiple levels,enhancing segmentation accuracy.By achieving remarkable results,our approach surpasses 369 competing teams in the 2020 Multimodal Brain Tumor Segmentation Challenge.With a Dice similarity coefficient of 0.89 and a Hausdorff distance of 4.8 mm,the DHA-ISSP model demonstrates its effectiveness in accurately segmenting brain tumors.We also extract radio mic characteristics from the segmented tumor areas using the DHA-ISSP model.By applying cross-validation of decision trees to the selected features,we identify crucial predictors for glioma survival,enabling personalized treatment strategies.Utilizing the DHA-ISSP model and the desired features,we assess patients’overall survival and categorize survivors into short,mid,in addition to long survivors.The proposed work achieved impressive performance metrics,including the highest accuracy of 0.91,precision of 0.84,recall of 0.92,F1 score of 0.88,specificity of 0.94,sensitivity of 0.92,area under the curve(AUC)value of 0.96,and the lowest mean absolute error value of 0.09 and mean squared error value of 0.18.These results clearly demonstrate the superiority of the proposed system in accurately segmenting brain tumors and predicting survival outcomes,highlighting its significant merit and potential for clinical applications.展开更多
In recent days,Deep Learning(DL)techniques have become an emerging transformation in the field of machine learning,artificial intelligence,computer vision,and so on.Subsequently,researchers and industries have been hi...In recent days,Deep Learning(DL)techniques have become an emerging transformation in the field of machine learning,artificial intelligence,computer vision,and so on.Subsequently,researchers and industries have been highly endorsed in the medical field,predicting and controlling diverse diseases at specific intervals.Liver tumor prediction is a vital chore in analyzing and treating liver diseases.This paper proposes a novel approach for predicting liver tumors using Convolutional Neural Networks(CNN)and a depth-based variant search algorithm with advanced attention mechanisms(CNN-DS-AM).The proposed work aims to improve accuracy and robustness in diagnosing and treating liver diseases.The anticipated model is assessed on a Computed Tomography(CT)scan dataset containing both benign and malignant liver tumors.The proposed approach achieved high accuracy in predicting liver tumors,outperforming other state-of-the-art methods.Additionally,advanced attention mechanisms were incorporated into the CNN model to enable the identification and highlighting of regions of the CT scans most relevant to predicting liver tumors.The results suggest that incorporating attention mechanisms and a depth-based variant search algorithm into the CNN model is a promising approach for improving the accuracy and robustness of liver tumor prediction.It can assist radiologists in their diagnosis and treatment planning.The proposed system achieved a high accuracy of 95.5%in predicting liver tumors,outperforming other state-of-the-art methods.展开更多
In the modern world,one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy(DR),which will result in retinal damage,and,thus,lead to blindness.Diabetic retinopathy(DR)can be well ...In the modern world,one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy(DR),which will result in retinal damage,and,thus,lead to blindness.Diabetic retinopathy(DR)can be well treated with early diagnosis.Retinal fundus images of humans are used to screen for lesions in the retina.However,detecting DR in the early stages is challenging due to the minimal symptoms.Furthermore,the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the condition.Nevertheless,the resources required for manually identifying the lesions are high.Similarly,training for Convolutional Neural Networks(CNN)is more time-consuming.This proposed research aims to improve diabetic retinopathy diagnosis by developing an enhanced deep learning model(EDLM)for timely DR identification that is potentially more accurate than existing CNN-based models.The proposed model will detect various lesions from retinal images in the early stages.First,characteristics are retrieved from the retinal fundus picture and put into the EDLM for classification.For dimensionality reduction,EDLM is used.Additionally,the classification and feature extraction processes are optimized using the stochastic gradient descent(SGD)optimizer.The EDLM’s effectiveness is assessed on the KAG-GLE dataset with 3459 retinal images,and results are compared over VGG16,VGG19,RESNET18,RESNET34,and RESNET50.Experimental results show that the EDLM achieves higher average sensitivity by 8.28%for VGG16,by 7.03%for VGG19,by 5.58%for ResNet18,by 4.26%for ResNet 34,and by 2.04%for ResNet 50,respectively.展开更多
基金study conception and design:S.Kannan,S.Anusuyadata collection:S.Kannan+1 种基金analysis and interpretation of results:S.Kannan,S.Anusuyadraft manuscript preparation:S.Kannan.All authors reviewed the results and approved the final version of the manuscript.
文摘Gliomas,the most prevalent primary brain tumors,require accurate segmentation for diagnosis and risk assess-ment.In this paper,we develop a novel deep learning-based method,the Dynamic Hierarchical Attention for Improved Segmentation and Survival Prognosis(DHA-ISSP)model.The DHA-ISSP model combines a three-band 3D convolutional neural network(CNN)U-Net architecture with dynamic hierarchical attention mechanisms,enabling precise tumor segmentation and survival prediction.The DHA-ISSP model captures fine-grained details and contextual information by leveraging attention mechanisms at multiple levels,enhancing segmentation accuracy.By achieving remarkable results,our approach surpasses 369 competing teams in the 2020 Multimodal Brain Tumor Segmentation Challenge.With a Dice similarity coefficient of 0.89 and a Hausdorff distance of 4.8 mm,the DHA-ISSP model demonstrates its effectiveness in accurately segmenting brain tumors.We also extract radio mic characteristics from the segmented tumor areas using the DHA-ISSP model.By applying cross-validation of decision trees to the selected features,we identify crucial predictors for glioma survival,enabling personalized treatment strategies.Utilizing the DHA-ISSP model and the desired features,we assess patients’overall survival and categorize survivors into short,mid,in addition to long survivors.The proposed work achieved impressive performance metrics,including the highest accuracy of 0.91,precision of 0.84,recall of 0.92,F1 score of 0.88,specificity of 0.94,sensitivity of 0.92,area under the curve(AUC)value of 0.96,and the lowest mean absolute error value of 0.09 and mean squared error value of 0.18.These results clearly demonstrate the superiority of the proposed system in accurately segmenting brain tumors and predicting survival outcomes,highlighting its significant merit and potential for clinical applications.
文摘In recent days,Deep Learning(DL)techniques have become an emerging transformation in the field of machine learning,artificial intelligence,computer vision,and so on.Subsequently,researchers and industries have been highly endorsed in the medical field,predicting and controlling diverse diseases at specific intervals.Liver tumor prediction is a vital chore in analyzing and treating liver diseases.This paper proposes a novel approach for predicting liver tumors using Convolutional Neural Networks(CNN)and a depth-based variant search algorithm with advanced attention mechanisms(CNN-DS-AM).The proposed work aims to improve accuracy and robustness in diagnosing and treating liver diseases.The anticipated model is assessed on a Computed Tomography(CT)scan dataset containing both benign and malignant liver tumors.The proposed approach achieved high accuracy in predicting liver tumors,outperforming other state-of-the-art methods.Additionally,advanced attention mechanisms were incorporated into the CNN model to enable the identification and highlighting of regions of the CT scans most relevant to predicting liver tumors.The results suggest that incorporating attention mechanisms and a depth-based variant search algorithm into the CNN model is a promising approach for improving the accuracy and robustness of liver tumor prediction.It can assist radiologists in their diagnosis and treatment planning.The proposed system achieved a high accuracy of 95.5%in predicting liver tumors,outperforming other state-of-the-art methods.
文摘In the modern world,one of the most severe eye infections brought on by diabetes is known as diabetic retinopathy(DR),which will result in retinal damage,and,thus,lead to blindness.Diabetic retinopathy(DR)can be well treated with early diagnosis.Retinal fundus images of humans are used to screen for lesions in the retina.However,detecting DR in the early stages is challenging due to the minimal symptoms.Furthermore,the occurrence of diseases linked to vascular anomalies brought on by DR aids in diagnosing the condition.Nevertheless,the resources required for manually identifying the lesions are high.Similarly,training for Convolutional Neural Networks(CNN)is more time-consuming.This proposed research aims to improve diabetic retinopathy diagnosis by developing an enhanced deep learning model(EDLM)for timely DR identification that is potentially more accurate than existing CNN-based models.The proposed model will detect various lesions from retinal images in the early stages.First,characteristics are retrieved from the retinal fundus picture and put into the EDLM for classification.For dimensionality reduction,EDLM is used.Additionally,the classification and feature extraction processes are optimized using the stochastic gradient descent(SGD)optimizer.The EDLM’s effectiveness is assessed on the KAG-GLE dataset with 3459 retinal images,and results are compared over VGG16,VGG19,RESNET18,RESNET34,and RESNET50.Experimental results show that the EDLM achieves higher average sensitivity by 8.28%for VGG16,by 7.03%for VGG19,by 5.58%for ResNet18,by 4.26%for ResNet 34,and by 2.04%for ResNet 50,respectively.
