This research focuses on the automatic detection and grading of microaneurysms in fundus images of diabetic retinopathy using artificial intelligence deep learning algorithms.By integrating multi-source fundus image d...This research focuses on the automatic detection and grading of microaneurysms in fundus images of diabetic retinopathy using artificial intelligence deep learning algorithms.By integrating multi-source fundus image data and undergoing a rigorous preprocessing workflow,a hybrid deep learning model architecture combining a modified U-Net and a residual neural network was adopted for the study.The experimental results show that the model achieved an accuracy of[X]%in microaneurysm detection,with a recall rate of[Y]%and a precision rate of[Z]%.In terms of grading diabetic retinopathy,the Cohen’s kappa coefficient for agreement with clinical grading was[K],and there were specific sensitivities and specificities for each grade.Compared with traditional methods,this model has significant advantages in processing speed and result consistency.However,it also has limitations such as insufficient data diversity,difficulties for the algorithm in detecting microaneurysms in severely hemorrhagic images,and high computational costs.The results of this research are of great significance for the early screening and clinical diagnosis decision support of diabetic retinopathy.In the future,it is necessary to further optimize the data and algorithms and promote clinical integration and telemedicine applications.展开更多
The quality of salted eggs differs in curing process.They need to be tested and graded before factory packaging.The dynamic images of salted eggs were acquired on conveyor.Firstly,preprocessing of color images must be...The quality of salted eggs differs in curing process.They need to be tested and graded before factory packaging.The dynamic images of salted eggs were acquired on conveyor.Firstly,preprocessing of color images must be done:the target area of the binary image was determined by mathematical morphology and removal of the object of a small area.According to the binary image is a convex or concave figure,the target region light leaked or not was determined.The effects of leaked region were eliminated by searching for mutation points,fitting salted egg boundary by the Least Square algorithm,labeling the binary image and extracting single target area.Then,six characteristic parameters were extracted in color space,and quality testing model was established by minimum error probability.The experimental results indicated that the detection accuracy reached above 93%and classification efficiency was 5400/h.It is proved the model is feasible for salted egg grading.展开更多
文摘This research focuses on the automatic detection and grading of microaneurysms in fundus images of diabetic retinopathy using artificial intelligence deep learning algorithms.By integrating multi-source fundus image data and undergoing a rigorous preprocessing workflow,a hybrid deep learning model architecture combining a modified U-Net and a residual neural network was adopted for the study.The experimental results show that the model achieved an accuracy of[X]%in microaneurysm detection,with a recall rate of[Y]%and a precision rate of[Z]%.In terms of grading diabetic retinopathy,the Cohen’s kappa coefficient for agreement with clinical grading was[K],and there were specific sensitivities and specificities for each grade.Compared with traditional methods,this model has significant advantages in processing speed and result consistency.However,it also has limitations such as insufficient data diversity,difficulties for the algorithm in detecting microaneurysms in severely hemorrhagic images,and high computational costs.The results of this research are of great significance for the early screening and clinical diagnosis decision support of diabetic retinopathy.In the future,it is necessary to further optimize the data and algorithms and promote clinical integration and telemedicine applications.
基金supported by National Natural Science Foundation of China(31371771)Special Fund for Agro-scientific Research in the Public Interest(201303084)National Key Technology Research and Development Program Project(2015BAD19B05).
文摘The quality of salted eggs differs in curing process.They need to be tested and graded before factory packaging.The dynamic images of salted eggs were acquired on conveyor.Firstly,preprocessing of color images must be done:the target area of the binary image was determined by mathematical morphology and removal of the object of a small area.According to the binary image is a convex or concave figure,the target region light leaked or not was determined.The effects of leaked region were eliminated by searching for mutation points,fitting salted egg boundary by the Least Square algorithm,labeling the binary image and extracting single target area.Then,six characteristic parameters were extracted in color space,and quality testing model was established by minimum error probability.The experimental results indicated that the detection accuracy reached above 93%and classification efficiency was 5400/h.It is proved the model is feasible for salted egg grading.
