Cross-Validation Convolution Neural Network-Based Algorithm for Automated Detection of Diabetic Retinopathy

The substantial vision loss due to Diabetic Retinopathy(DR)mainly damages the blood vessels of the retina.These feature changes in the blood vessels fail to exist any manifestation in the eye at its initial stage,if this problem doesn’t exhibit initially,that leads to permanent blindness.So,this type of disorder can be only screened and identified through the processing of fundus images.The different stages in DR are Micro aneurysms(Ma),Hemorrhages(HE),and Exudates,and the stages in lesion show the chance of DR.For the advancement of early detection of DR in the eye we have developed the CNN-based identification approach on the fundus blood lesion image.The CNN-based automated detection of DR proposes the novel Graph cutter-built background and foreground superpixel segmentation technique and the foremost classification of fundus images feature was done through hybrid classifiers as K-Nearest Neighbor(KNN)classifier,Support Vector Machine(SVM)classifier,and Cascaded Rotation Forest(CRF)classifier.Over this classifier,the feature cross-validation made the classification more accurate and the comparison is made with the previous works of parameters such as specificity,sensitivity,and accuracy shows that the hybrid classifier attains excellent performance and achieves an overall accuracy of 98%.Among these Cascaded Rotation Forest(CRF)classifier has more accuracy than others.

Robust Error Density Estimation in Ultrahigh Dimensional Sparse Linear Model

This paper focuses on error density estimation in ultrahigh dimensional sparse linear model,where the error term may have a heavy-tailed distribution.First,an improved two-stage refitted crossvalidation method combined with some robust variable screening procedures such as RRCS and variable selection methods such as LAD-SCAD is used to obtain the submodel,and then the residual-based kernel density method is applied to estimate the error density through LAD regression.Under given conditions,the large sample properties of the estimator are also established.Especially,we explicitly give the relationship between the sparsity and the convergence rate of the kernel density estimator.The simulation results show that the proposed error density estimator has a good performance.A real data example is presented to illustrate our methods.

Predicting microRNA-disease association based on microRNA structural and functional similarity network

Background:Increasing evidences indicate that microRNAs (miRNAs) are functionally related to the development and progression of various human diseases.Inferring disease-related miRNAs can be helpful in promoting disease biomarker detection for the treatment,diagnosis,and prevention of complex diseases.Methods:To improve the prediction accuracy of miRNA-disease association and capture more potential diseaserelated miRNAs,we constructed a precise miRNA global similarity network (MSFSN) via calculating the miRNA similarity based on secondary structures,families,and functions.Results:We tested the network on the classical algorithms:WBSMDA and RWRMDA through the method of leaveone- out cross-validation.Eventually,AUCs of 0.8212 and 0.9657 are obtained,respectively.Also,the proposed MSFSN is applied to three cancers for breast neoplasms,hepatocellular carcinoma,and prostate neoplasms.Consequently,82%,76%,and 82% of the top 50 potential miRNAs for these diseases are respectively validated by the miRNA-disease associations database miR2Disease and oncomiRDB.Conclusion:Therefore,MSFSN provides a novel miRNA similarity network combining precise function network with global structure network of miRNAs to predict the associations between miRNAs and diseases in various models.