Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks. Learning can be supervised, semi-supervised or unsupervised. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced results comparable to and in some cases superior to human experts.
Artificial Neural Networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains. Specifically, neural networks tend to be static and symbolic, while the biological brain of most living organisms is dynamic (plastic) and analog.
A deep learning system (DLS) uses artificial intelligence and representation learning methods to process large data and extract meaningful patterns. A few DLSs have recently shown high sensitivity and specificity (>90%) in detecting referable diabetic retinopathy from retinal photographs, primarily using high-quality images from publicly available databases from homogenous populations of white individuals. The performance of a DLS in screening for diabetic retinopathy should ideally be evaluated in clinical or population settings in which retinal images from patients of different races and ethnicities (and therefore with varying fundi pigmentation) have varying qualities (e.g., due to poor pupil dilation, media opacity, poor contrast or focus). Furthermore, in screening programs for diabetic retinopathy, the detection of incidental but related vision-threatening eye diseases, such as glaucoma and age-related macular degeneration (AMD), should be incorporated because missing such cases is clinically unacceptable.
Aims and Scope:
- Retinal Disease Detection
- Retinal Imaging
- Cataract, Glaucoma, Diabetic Retinopathy Detection and Grading
- Retinal Image Classification
- Identification and Classification of Ophthalmic Diseases
- Detection of Visual Diseases from Medical Images