Abstract: Disclosed is a method for training a neural network to quantify the vessel calibre of retina fundus images. The method involves receiving a plurality of fundus images; pre-processing the fundus images to normalise images features of the fundus images; and training a multi-layer neural network, the neural network comprising of a convolutional unit, multiple dense blocks alternating with transition units for down-sampling image features determined by the neural network, and a fully-connected unit, wherein each dense block comprises a series of cAdd units packed with multiple convolutions, and each transition layer comprises a convolution with pooling.

Abstract: Disclosed is a method for training a neural network to quantify the vessel calibre of retina fundus images. The method involves receiving a plurality of fundus images; pre-processing the fundus images to normalise images features of the fundus images; and training a multi-layer neural network, the neural network comprising of a convolutional unit, multiple dense blocks alternating with transition units for down-sampling image features determined by the neural network, and a fully-connected unit, wherein each dense block comprises a series of cAdd units packed with multiple convolutions, and each transition layer comprises a convolution with pooling.

Abstract: A method of modifying a retina fundus image for a deep learning model is disclosed. In a described embodiment, the method includes converting a retina fundus image to a binary image by converting pixels of the retina fundus image to low intensity modified pixels and high intensity modified pixels of the binary image, and determining a first boundary between the low intensity modified pixels and high intensity modified pixels of the binary image. The method further includes removing outlier boundary values from the first boundary, constructing a second boundary from remaining boundary values, identifying the pixels of the retina fundus image that are within the second boundary, and constructing a modified retina fundus image containing the identified pixels for a deep learning model.

Abstract: A method of modifying a retina fundus image for a deep learning model is disclosed. In a described embodiment, the method includes converting a retina fundus image to a binary image by converting pixels of the retina fundus image to low intensity modified pixels and high intensity modified pixels of the binary image, and determining a first boundary between the low intensity modified pixels and high intensity modified pixels of the binary image. The method further includes removing outlier boundary values from the first boundary, constructing a second boundary from remaining boundary values, identifying the pixels of the retina fundus image that are within the second boundary, and constructing a modified retina fundus image containing the identified pixels for a deep learning model.

Abstract: A platform is proposed for automated analysis of retinal images, for obtaining from them information characterizing retinal blood vessels which may be useful in forming a diagnosis of a medical condition. A first aspect of the invention proposes that a plurality of characteristics of the retina are extracted, in order to provide data which is useful for enabling an evaluation of cardiovascular risk prediction, or even diagnosis of a cardiovascular condition. A second aspect uses fractal analysis of retinal images to provide vascular disease risk prediction, such as, but not limited to, diabetes and hypertension.

Abstract: A platform is proposed for automated analysis of retinal images, for obtaining from them information characterizing retinal blood vessels which may be useful in forming a diagnosis of a medical condition. A first aspect of the invention proposes that a plurality of characteristics of the retina are extracted, in order to provide data which is useful for enabling an evaluation of cardiovascular risk prediction, or even diagnosis of a cardiovascular condition. A second aspect uses fractal analysis of retinal images to provide vascular disease risk prediction, such as, but not limited to, diabetes and hypertension.