The aim of this study is to detect whether the quantitative textural features of optical coherence tomography angiography (OCTA) images can be used to detect the eyes in the early stage of diabetic retinopathy (DR) from eyes with diabetes and no DR (NDR). Textural features including fractal dimension, contrast, correlation, entropy, energy, and homogeneity were calculated from the OCTA images. The Student’s $t$ -test was performed to identify the textural features that can be able to detect DR in the early stage. The area under the receiver operating characteristic (AUROC) curves, sensitivity, and specificity were calculated between the study groups. Our results indicated that the fractal dimension in ICP and SVP and the correlation in SVC showed the statistical significance between mild NPDR patients and NDR patients. The ROC analysis results showed that the AUROC of the fractal dimension in ICP was 0.736 with 0.773 sensitivity and 0.700 specificity. The cutoff point in ICP was 2.616. The OCTA-based fractal dimension was able to discriminate diabetic eyes with early retinopathy from healthy and NDR with higher sensitivity and specificity. The OCTA-based correlation showed the power to differentiate the mild NPDR eyes from the normal healthy and diabetic eyes. These results suggest that texture-based features of OCTA have the potential to assist in the assessment of therapeutic interventions to prevent early DR in diabetic subjects.

This paper presents a methodology to evaluate the safety of the management in the chemical factory with the back propagation neural network (BPNN) and the radial basis function neural network (RBFNN). By comparing the evaluated values with the experts' scores, the result indicates that the RBFNN is capable of evaluating and predicting the safety in management of the chemical factory

The optical coherence tomography (OCT) technique is applied in the diagnosis of the skin tissue. In general, quantitative imaging features obtained from OCT images have already been used as biomarkers to categorize skin tumors. Particularly, the fractal dimension (FD) could be capable of providing an efficient approach for analyzing OCT images of skin tumors. The 2D Fourier fractal analysis (FFA) as well as the differential box counting method (DBCM) was used in this paper to classify the basal cell carcinomas (BCC), melanomas, and benign melanocytic nevi. Generalized estimating equations were used to test for differences between skin tumors. Our results showed that the significant decrease of the 2D FD was detected in the benign melanocytic nevi and basal cell carcinomas as compared with the melanomas. Our results also suggested that the 2D FFA could provide a more efficient way to calculating FD to differentiate the basal cell carcinomas, melanomas, and benign melanocytic nevi as compared to the 2D DBCM.

This paper presented a methodology to assess the operation safety of the urban rail transit system by using the generalized regression neural network. By comparing the evaluated values with the graded scores of the experts, the results indicated that predictions by using the generalized regression neural network only have 5.38% averaged relative error compared with experts’ scores. Therefore, the GRNN was capable of evaluating and predicting the operation safety of the urban rail transit system.