Needs/Requirements and Design of Retinal Imaging and Image Processing Methods for Diabetic Retinopathy in the Indian Context

High glucose level disrupts the structure of the retinal layer in the eyes and causes diabetic retinopathy which is characterized with new pathologic blood vessels in the eyes. Although diabetic retinopathy is not clear at the beginning of the disease, it is the most common problem in people who have diabetes and causes blindness or cloudy vision if it is not diagnosed at the beginning of the disease. For early diagnosis of diabetic retinopathy, regular fundus controls and examination of the edema in the vessels of the retina are made periodically by ophthalmologists. With in the scope of this study, it is made possible to provide the early diagnosis and the level of diabetic retinopathy by using deep learning, image processing methods, and convolutional neural networks of the retina. In order to provide ease and rapid of diagnosis of the diabetic retinopathy in daily life, the diagnosis protocol has been turned into a mobile application. With the mobile application, both the diagnosis and more regular results of the diabetic retinopathy can be obtained easily and practically.

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Diabetic Retinal Grading Using Attention-Based Bilinear Convolutional Neural Network and Complement Cross Entropy

Entropy ◽

10.3390/e23070816 ◽

2021 ◽

Vol 23 (7) ◽

pp. 816

Author(s):

Pingping Liu ◽

Xiaokang Yang ◽

Baixin Jin ◽

Qiuzhan Zhou

Keyword(s):

Neural Network ◽

Image Processing ◽

Diabetic Retinopathy ◽

Convolutional Neural Network ◽

Image Classification ◽

Network Model ◽

Rapid Development ◽

Image Data ◽

Lesion Detection ◽

Great Success

Diabetic retinopathy (DR) is a common complication of diabetes mellitus (DM), and it is necessary to diagnose DR in the early stages of treatment. With the rapid development of convolutional neural networks in the field of image processing, deep learning methods have achieved great success in the field of medical image processing. Various medical lesion detection systems have been proposed to detect fundus lesions. At present, in the image classification process of diabetic retinopathy, the fine-grained properties of the diseased image are ignored and most of the retinopathy image data sets have serious uneven distribution problems, which limits the ability of the network to predict the classification of lesions to a large extent. We propose a new non-homologous bilinear pooling convolutional neural network model and combine it with the attention mechanism to further improve the network’s ability to extract specific features of the image. The experimental results show that, compared with the most popular fundus image classification models, the network model we proposed can greatly improve the prediction accuracy of the network while maintaining computational efficiency.

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Comparison of Different Image Processing Methods with Spatial Information in Clinical Brain MRI

Proceedings of the 2020 12th International Conference on Bioinformatics and Biomedical Technology ◽

10.1145/3405758.3405786 ◽

2020 ◽

Author(s):

Iza Sazanita Isa ◽

Mohamad Khairul Faizi Mat Saad ◽

Muhammad Haris Khusairi Mohmad Kadir ◽

Ahmad Afifi Ahmad Afandi ◽

Noor Khairiah A. Karim ◽

...

Keyword(s):

Image Processing ◽

Spatial Information ◽

Brain Mri ◽

Processing Methods

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Image Processing Methods of LANDSAT TM Data for Lineament Analysis

GEOINFORMATICS ◽

10.6010/geoinformatics1975.1989.14b_25 ◽

1989 ◽

Vol 1989 (14B) ◽

pp. 25-39

Author(s):

Katsuaki KOIKE ◽

Hiroyuki ITOH ◽

Michito OHMI

Keyword(s):

Image Processing ◽

Landsat Tm ◽

Processing Methods ◽

Lineament Analysis

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Fast Transforms in Image Processing: Compression, Restoration, and Resampling

Advances in Electrical Engineering ◽

10.1155/2014/276241 ◽

2014 ◽

Vol 2014 ◽

pp. 1-23 ◽

Cited By ~ 6

Author(s):

Leonid P. Yaroslavsky

Keyword(s):

Image Processing ◽

Image Compression ◽

Image Restoration ◽

Compressive Sensing ◽

Adaptive Filters ◽

Processing Methods ◽

Fast Transforms ◽

Image Transforms

Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet, and alike. They proved to be very efficient in image compression, in image restoration, in image resampling, and in geometrical transformations and can be traced back to early 1970s. The paper reviews these methods, with emphasis on their comparison and relationships, from the very first steps of transform image compression methods to adaptive and local adaptive filters for image restoration and up to “compressive sensing” methods that gained popularity in last few years. References are made to both first publications of the corresponding results and more recent and more easily available ones. The review has a tutorial character and purpose.

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