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RetoNet: a deep learning architecture for automated retinal ailment detection
摘要： Researchers are trying to tap the immense potential of big data to revolutionize all aspects of societal activity and to assist in having well informed decisions. Healthcare being one such field where proper analytics of available big medical data can lead to early detection and treatment of many ailments. Machine learning played a significant role in the design of automated diagnostic systems and today we have deep learning models in this arena which are outperforming human expertise in terms of predictive accuracy. This paper proposes RetoNet, a convolutional neural network architecture, which is trained and optimized to detect retinal ailment from fundus images with pronounced accuracy and its performance is also proven to be superior to a transfer learning based model developed for the same. Deep learning based e-diagnostic system can be an accurate, cost effective and convenient solution for the shortage of expertise on demand in the healthcare field.
关键词： Convolutional neural network,E-health,Retinal disease detection,ANN,Deep learning
更新于2025-09-19 17:15:36
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[IEEE 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) - Coimbatore, India (2018.3.1-2018.3.3)] 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) - Processing Retinal Images to Discover Diseases
摘要： The retina of a human eye consists of billion of photosensitive cells (rods and cones) and alternative nerve cells that acquire and arrange visual information. The retina of a human eye is a thin tissue layer on the inside back wall of your eye. Three of the are Diabetic retinal diseases most Retinopathy, Glaucoma, and Cataract. The world is presently experiencing an epidemic of Diabetic Retinopathy (DR). Current predictions draw an estimation of doubling of the number affected from the current 170 million to an estimated 367 million by 2030. We propose a system wherein we extract blood vessels of the retina to detect eye diseases. Manually extracting the blood vessels of the human retina is a time-consuming task, and thus an automation of this process results in easy implementation of the work. This paper aims to design and consequently implement deep convolutional neural networks to identify the presence of an exudate, and thereby classify it into Diabetic Retinopathy, Glaucoma, and/or Cataract.
关键词： Computer vision,Glaucoma,Diabetic Retinopathy,Cataract,Convolutional Neural Networks,Retinal disease detection,CNN
更新于2025-09-04 15:30:14