Impact of eye fundus image preprocessing on key objects segmentation for glaucoma identification

The pathological changes in the eye fundus image, especially around Optic Disc (OD) and Optic Cup (OC) may indicate eye diseases such as glaucoma. Therefore, accurate OD and OC segmentation is essential. The variety in images caused by different eye fundus cameras makes the complexity for the existing deep learning (DL) networks in OD and OC segmentation. In most research cases, experiments were conducted on individual data sets only and the results were obtained for that specific data sample. Our future goal is to develop a DL method that segments OD and OC in any kind of eye fundus image but the application of the mixed training data strategy is in the initiation stage and the image preprocessing is not discussed. Therefore, the aim of this paper is to evaluate the mage preprocessing impact on OD and OC segmentation in different eye fundus images aligned by size. We adopted a mixed training data strategy by combining images of DRISHTI-GS, REFUGE, and RIM-ONE datasets, and applied image resizing incorporating various interpolation methods, namely bilinear, nearest neighbor, and bicubic for image resolution alignment. The impact of image preprocessing on OD and OC segmentation was evaluated using three convolutional neural networks Attention U-Net, Residual Attention U-Net (RAUNET), and U-Net++. The experimental results show that the most accurate segmentation is achieved by resizing images to a size of 512 x 512 px and applying bicubic interpolation. The highest Dice of 0.979 for OD and 0.877 for OC are achieved on  RISHTI-GS test dataset, 0.973 for OD and 0.874 for OC on the REFUGE test dataset, 0.977 for OD and 0:855 for OC on RIM-ONE test dataset. Anova and Levene’s tests with statistically significant evidence at α = 0.05 show that the chosen size in image resizing has impact on the OD and OC segmentation results, meanwhile, the interpolation method does influent OC segmentation only

Application of vector autoregression (VAR) models for the analysis of economic processes

Nagrinėjamas bendras vidaus produktas (BVP) to meto kainomis, išmatuotas išlaidų metodu. Pagrindinis darbo tikslas – ekonominiams procesams analizuoti pritaikyti vektorinės autoregresijos (VAR) modelius. Siekiant efektyviai įvertinti VAR, nustatomas optimalus vėlavimų (lagų) lygis atsižvelgiant į Akaike informacinio kriterijaus (AICC) ir determinacijos koeficiento reikšmes bei atliekant vėlavimų (lagų) testus: Lag Length Criteria ir Lag Exclusion. Įvertinus lagus ir reikšmingus kintamuosius, gaunama, kad BVP, išmatuotam išlaidų metodu, tiksliausias yra VAR(9) modelis. Trumpai aptariami VARX modeliai, kurie gaunami į vektorinės autoregresijos modelius įtraukus egzogeninius kintamuosius.Summary. The gross domestic product (GDP) at current prices, measured by expenditure approach, was examined. The aim of this study is to adapt vector autoregression (VAR) models for the analysis of economic processes. In order to effectively evaluate the VAR, the optimal level of delays (lags) is determined according to the Akaike information criterion (AICC) and the coefficient of determination values and delays (lags) in the testing: Lag Length Criteria and Lag Exclusion. The evaluation of delays (lags) and significant variables, obtained, that GDP, measured by expenditure approach, accurate model is VAR (9). VARX models, which are generated in the vector autoregression models for the inclusion of exogenous variables, are briefly discussed. Keywords: GDP, vector autoregression (VAR) models, VARX model, AICC criterio

Deep learning methods for glaucoma identification using digital fundus images

In this survey we analyzed the literature, evaluated the methods for glaucoma identification and identified the main issues faced by other researchers. From the literature it is observed that most of the computer aided diagnosis (CAD) tools for identification of pathological changes in eye fundus are in the early stage of development. The accuracy of glaucoma classification achieved by different methods ranges from 87.50% to 99.41%. However, the classification results are obtained with different data sets and different quality images. Therefore, the further research would be needed to create an algorithm using a data set contained of wider range and various quality images. Also, it is necessary to estimate the advantages and disadvantages of the existing methods and to compare the obtained classification results under the same conditions of experiments