Diabetic Retinopathy (DR) stands as the leading cause of blindness globally, particularly affecting individuals between the ages of 20 and 70. This paper presents a Computer-Aided Diagnosis (CAD) system designed for the automatic classification of retinal images into five distinct classes: Normal, Mild, Moderate, Severe, and Proliferative Diabetic Retinopathy (PDR). The proposed system leverages Convolutional Neural Networks (CNNs) employing pre-trained deep learning models. Through the application of fine-tuning techniques, our model is trained on fundus images of diabetic retinopathy with resolutions of 350x350x3 and 224x224x3. Experimental results obtained on the Kaggle platform, utilizing resources comprising 4 CPUs, 17 GB RAM, and 1 GB Disk, demonstrate the efficacy of our approach. The achieved Area Under the Curve (AUC) values for CNN, MobileNet, VGG-16, InceptionV3, and InceptionResNetV2 models are 0.50, 0.70, 0.53, 0.63, and 0.69, respectively.

Updated Facial recognition technology is being employed in more UK schools to allow pupils to pay for their meals, according to reports today. In North Ayrshire Council, a Scottish authority encompassing the Isle of Arran, nine schools are set to begin processing meal payments for school lunches using facial scanning technology. The authority and the company implementing the technology, CRB Cunninghams, claim the system will help reduce queues and is less likely to spread COVID-19 than card payments and fingerprint scanners, according to the Financial Times. Speaking to the publication, David Swanston, the MD of supplier CRB Cunninghams, said the cameras verify the child's identity against "encrypted faceprint templates", and will be held on servers on-site at the 65 schools that have so far signed up. He added: "In a secondary school you have around about a 25-minute period to serve potentially 1,000 pupils. So we need fast throughput at the point of sale."