Automated Glaucoma Screening from Retinal Fundus Image Using Deep Learning

The present study relates to diagnostic devices, and more specifically, to a diabetic retinopathy prediction device, system and method for early prediction of diabetic retinopathy with application of deep learning. The device includes an image capturing device, a memory coupled to processor. The image capturing device obtains a retinal fundus image from the user. The memory comprising executable instructions which upon execution by the processor configures the device to obtain physiological parameters of the user in real-time from the image capturing device, retrieve the obtained retinal fundus image and the one or more obtained physiological parameters and compare the one or more extracted features with at least one pre-stored feature in a database to generate at least a prediction result indicative of detection of the presence, the progression or the treatment effect of the disease in the user.

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Detection of Nuclear Cataract in Retinal Fundus Image using RadialBasis FunctionbasedSVM

2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC) ◽

10.1109/pdgc50313.2020.9315834 ◽

2020 ◽

Author(s):

Manoj Kumar Behera ◽

S. Chakravarty ◽

Apurwa Gourav ◽

Satyabrata Dash

Keyword(s):

Nuclear Cataract ◽

Fundus Image ◽

Retinal Fundus Image ◽

Retinal Fundus

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Image Preprocessing in Classification and Identification of Diabetic Eye Diseases

Data Science and Engineering ◽

10.1007/s41019-021-00167-z ◽

2021 ◽

Author(s):

Rubina Sarki ◽

Khandakar Ahmed ◽

Hua Wang ◽

Yanchun Zhang ◽

Jiangang Ma ◽

...

Keyword(s):

Image Processing ◽

Image Quality ◽

Geometric Transformation ◽

Diabetic Patients ◽

Fundus Image ◽

Classification Problems ◽

Specificity And Sensitivity ◽

Retinal Fundus Image ◽

Retinal Fundus ◽

Traditional Image

AbstractDiabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.

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