Diabetic Retinopathy (DR) grading into different stages of severity continues to remain a challenging issue due to the complexities of the disease. Diabetic Retinopathy grading classifies retinal images to five levels of severity ranging from 0 to 5, which represents No DR, Mild non-proliferative diabetic retinopathy (NPDR), Moderate NPDR, Severe NPDR, and proliferative diabetic retinopathy. With the advancement of Deep Learning, studies on the application of the Convolutional Neural Network (CNN) in DR grading have been on the rise. High accuracy and sensitivity are the desired outcome of these studies. This paper reviewed recently published studies that employed CNN for DR grading to 5 levels of severity. Various approaches are applied in classifying retinal images which are, (i) by training CNN models to learn the features for each grade and (ii) by detecting and segmenting lesions using information about their location such as microaneurysms, exudates, and haemorrhages. Public and private datasets have been utilised by researchers in classifying retinal images for DR. The performance of the CNN models was measured by accuracy, specificity, sensitivity, and area under the curve. The CNN models and their performance varies for every study. More research into the CNN model is necessary for future work to improve model performance in DR grading. The Inception model can be used as a starting point for subsequent research. It will also be necessary to investigate the attributes that the model uses for grading.
The revolution of technology brings many benefits towards diverse population. Digital game is one of the digital technologies that has potential to facilitate older adults' daily routine. However, some of them faces challenges to adopt the usage of digital games in their daily lives, one of which is that most commercial games are not suitable for older people. This paper discusses the investigation into the challenges associated with the older adults' adoption of digital games, their interaction, and experiences with digital games and specifically explores the andragogical perspectives, and game design attributes. A set of questionnaires consisted of open-ended and close-ended questions were distributed, targeting the older adults across Malaysia, using online and non-probability sampling technique. 81 respondents were recruited, and 56 respondents (n=56) were eligible in this study. Four participants were recruited for informal interview session. The analysis of the results indicates that the older adults' perception of digital games and game design aspects are the major factors influencing their digital game adoption. Game designs are important to attract many older adults to experience and interact with digital games.
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