My New Project named Diatos (Diabetic Retinopathy diagnosis model and multimodal user interface) addresses the critical issue of early detection and diagnosis of Diabetic Retinopathy (DR), a leading cause of blindness globally.
DIATOS (Diabetic Retinopathy diagnosis model and multimodal user interface) addresses the critical issue of early detection and diagnosis of Diabetic Retinopathy (DR), a leading cause of blindness globally. The project leverages machine learning and AI to enhance diagnostic accuracy for retinal images, aiming to alleviate the burden on healthcare systems by enabling quicker, more reliable screening.
The project focuses on training a convolutional neural network (CNN), specifically a transfer learning model based on ResNet50, to classify retinal images from the Kaggle Diabetic Retinopathy Dataset. Starting with a 67% accuracy, DIATOS improves upon this by employing the Intel AI PC's Neural Processing Unit (NPU) for preprocessing and model training. The NPU accelerates computation, particularly in the preprocessing pipeline and during fine-tuning of the model, significantly boosting performance while reducing latency. ResNet50, already pretrained on the ImageNet dataset, benefits from this transfer learning approach, as it adapts to the retinal image domain more effectively.