Employing CNN ensemble models in classifying dental caries using oral photographs

Dental caries is arguably the most persistent dental condition that affects most people over their lives. Carious lesions are commonly diagnosed by dentists using clinical and visual examination along with oral radiographs. In many circumstances, dental caries is challenging to detect with photography and might be mistaken as shadows for various reasons, including poor photo quality. However, with the introduction of Artificial Intelligence and robotic systems in dentistry, photographs can be a helpful tool in oral epidemiological research for the assessment of dental caries prevalence among the population. It can be used particularly to create a new automated approach to calculate DMF (Decay, Missing, Filled) index score. In this paper, an autonomous diagnostic approach for detecting dental cavities in photos is developed using deep learning algorithms and ensemble methods. The proposed technique employs a set of pretrained models including Xception, VGG16, VGG19, and DenseNet121 to extract essential characteristics from photographs and to classify images as either normal or caries. Then, two ensemble learning methods, E- majority and E-sum, are employed based on majority voting and sum rule to boost the performances of the individual pretrained model. Experiments are conducted on 50 images with data augmentation for normal and caries images, the employed E-majority and E-sum achieved an accuracy score of 96% and 97%, respectively. The obtained results demonstrate the superiority of the proposed ensemble framework in the detection of caries. Furthermore, this framework is a step toward constructing a fully automated, efficient decision support system to be used in the dentistry area

The pass/fail grading system at Jordanian universities for online learning courses from students’ perspectives

This study aimed to investigate the perspectives of Jordanian university students toward the pass/fail grading system (PFGS) that was used during the COVID-19 pandemic. To achieve this goal, a questionnaire was prepared, consisting of 37 items in its final form; divided into four subscales: advantages, drawbacks of (PFGS), the reasons for its use by students, and their attitudes toward it. This questionnaire was applied to a sample of 6,404 male and female students from four Jordanian universities: Al al-Bayt University, Balqa Applied University, The Hashemite University, and The University of Jordan. Out of the 6,404 responses, we rejected 263 responses due to careless survey filling and/or incomplete answers. The results revealed that most students were satisfied with applying the PFGS to all courses, especially among the freshmen. They believed that the PFGS was the best choice for grading due to online exams and full distance learning lectures. The results showed significant differences at α = 0.05 in how students evaluated the PFGS; namely: its advantages, drawbacks, reasons, and their attitudes toward it, based on participants’ gender, school, and academic level. As for the relationship between GPA and students’ perspectives on the PFGS, it was clear that the correlation coefficients indicated weak but significant correlations