Convolutional Neural Network for Segmentation and Classification of Glaucoma

Glaucoma is an eye disease that is caused by elevated intraocular pressure and commonly leads to optic nerve damage. Thanks to its vital role in transmitting visual signals from the eye to the brain, the optic nerve is essential for maintaining good and clear vision. Glaucoma is considered one of the leading causes of blindness. Accordingly, the earlier doctors can diagnose and detect the disease, the more feasible its treatment becomes. Aiming to facilitate this task, this study proposes a method for detecting diseases by analyzing images of the interior of the eye using a convolutional neural network. This method consists of segmentation based on a modified U-Net architecture and classification using the DenseNet-201 technique. The proposed model utilized the DRISHTI-GS and RIM-ONE datasets to evaluate glaucoma images. These datasets served as valuable sources of diverse and representative glaucoma-related images, enabling a thorough evaluation of the model’s performance. Finally, the results were highly promising after subjecting the model to a thorough evaluation process. The segmentation accuracy reached 96.65%, while the classification accuracy reached 96.90%. This means that the model excelled in accurately delineating and isolating the relevant regions of interest within the eye images, such as the optical disc and optical cup, which are crucial for diagnosing glaucoma

Association between Non-Verbal Intelligence and Academic Performance of Schoolchildren from Taza, Eastern Morocco

Interest in identifying factors influencing educational success is growing. It is often observed that a group of students share the same external variables (school environment) yet have different results, which states that individual variables have more impact on the determination of academic performance. Therefore, the present study aimed to substantiate this fact by investigating the association between non-verbal fluid intelligence and academic performance in a population of schoolchildren in Eastern Morocco. The investigation was a cross-sectional study based on a self-administered questionnaire. Items included the standard Raven’s progressive matrices. Students’ grades were collected from the administrative offices of the visited schools. Significant and positive correlations between the non-verbal intelligence scores and the school results were found: for the general average, the correlation was 0.574; for the school subject French, the correlation coefficient was 0.475; and for mathematics, we found a relatively low coefficient of 0.381. Non-verbal fluid intelligence significantly and positively predicted academic performance (β = .574, p = .000). These results call for policymakers to implement the use of intelligence tests with school directors and teachers as a diagnostic tool to guide support efforts for low-achieving children and even to create pilot classes for the best-performing students