Fundus-DeepNet: Multi-label deep learning classification system for . . . Detecting multiple ocular diseases in fundus images is crucial in ophthalmic diagnosis This study introduces the Fundus-DeepNet system, an automated multi-label deep learning classification system designed to identify multiple ocular diseases by integrating feature representations from pairs of fundus images (e g , left and right eyes)
A comprehensive review of retinal disease diagnosis and open access . . . Data distribution: The effectiveness of the system can be impacted by the unequal distribution of data among different eye disease classification Insufficient data on specific diseases may result in biased predictions, as the system may encounter difficulties in learning the characteristics of less prevalent diseases
a retinal fundus dataset for eye disease diagnosis and fundus synthesis EDDFS contains 28877 color fundus images for deep learning-based diagnosis Except for 15000 healthy samples, the dataset consists of 8 eye disorders including diabetic retinopathy, agerelated macular degeneration, glaucoma, pathological myopia, hypertension, retinal vein occlusion, LASIK spot and others
Cross-Domain Multi-disease Ocular Disease Recognition via Data . . . In this paper, a cross-domain fundus image recognition framework based on deep neural networks with data enhancement is proposed First, the ResNeXt101 model is chosen as the basic framework Second, some data enhancement methods and focal loss are used to improve recognition performance
MultiEYE: Dataset and Benchmark for OCT-Enhanced Retinal Disease . . . To expand the scope of clinical applications, we formulate a novel setting, "OCT-enhanced disease recognition from fundus images", that allows for the use of unpaired multi-modal data during the training phase and relies on the widespread fundus photographs for testing
Ophthalmologist-Level Classification of Fundus Disease With Deep Neural . . . To implement the classification of fundus diseases using deep convolutional neural networks (CNN), which is trained end-to-end from fundus images directly, the only input are pixels and disease labels, and the output is a probability distribution of a fundus image belonging to 18 fundus diseases
Benchmarking deep models on retinal fundus disease diagnosis and a . . . Retinal fundus imaging contributes to monitoring the vision of patients by providing views of the interior surface of the eyes Machine learning models greatly aided ophthalmologists in detecting retinal disorders from color fundus images
Eye Disease Diagnosis and Fundus Synthesis: A Large-Scale Dataset and . . . Data-driven machine learning methods, especially deep learning models in recent years, provide automatic ophthalmological disease diagnosis techniques from color fundus images Data with high quality, diversity and balanced distribution supports deep model-based eye disease diagnosis
Ultra-wide-field fundus photography and AI-based screening and referral . . . Zhao and Gu et al develop a deep learning algorithm based on UWF fundus images to screen for up to 25 ocular fundus conditions Their study demonstrates the importance of the peripheral retinal region in fundus screening and highlights the potential of AI-based methods for large-scale screening applications