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Fundus Image Classification

Categories Medicine, Science

Essay, Pages 2 (413 words)

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Essay, Pages 2 (413 words)

Problem statement and background

Gulshan, V(2015) is of the view that ‘Computerized medicinal picture examination’ or ‘Fundus image classification’ is a rising field of research that distinguishes the sickness with the assistance of imaging innovation. Diabetic retinopathy (DR) is a retinal malady that is analyzed in diabetic patients.

‘Fundus image classification’ is generally used to group diabetic retinopathy from fundus pictures gathered from suspected people.

The proposed DR grouping framework accomplishes an evenly advanced arrangement through the mix of a Gaussian blend model (GMM), visual geometry gathering system (VGGNet), solitary worth disintegration (SVD) and guideline segment examination (PCA), and softmax, for area division, high dimensional element extraction, include determination and fundus picture characterization, separately(Sinha, R.

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A. 2015).

Objective and goals

The first objective of the research is based on a technique to improve exactness for seriousness order. This strategy has two highlights. The first is to remove Region-Of-Interest (ROI) sub-pictures concentrating on districts locally catching sores to limit foundation commotion in picture preprocessing for the characterization.

The second motivation goal behind the present research is to use the GNN which isn’t yet applied for fundus picture grouping. To assess our proposed technique, we utilize Indian Diabetic Retinopathy Image Dataset (IDRiD) used in “Diabetic Retinopathy” (Haloi, M. 2015).

Importance and impact of doing this project

The significance of the research could be analyzed when the people will be aware of the inspection of anomalies associated with diseases that affect the eye, and to monitor their progression. This project would also highlight glaucoma and multiple sclerosis, as well as monitor disease processes such as macular degeneration, retinal neoplasms, choroid disturbances and diabetic retinopathy (Cheriet, F.

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2015).

References:

  1. Gulshan, V.; Peng, L.; Coram, M.; Stumpe, M.C.; Wu, D.; Narayanaswamy, A.; Venugopalan, S.; Widner, K.; Madams, T.; Cuadros, (2016). Development and validation of a deep learning algorithm for the detection of diabetic retinopathy in retinal fundus photographs.
  2. Haloi, M.; Dandapat, S.; Sinha, R. A(2015). Gaussian scale-space approach for exudates detection, classification and severity prediction.
  3. Haloi, M(2015). Improved microaneurysm detection using deep neural networks.
  4. van Grinsven, M.J.; van Ginneken, B.; Hoyng, C.B.; Theelen, T.; S?nchez, C.I(2014). Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images.IEEE Trans. Med. Imaging.
  5. Srivastava, R.; Duan, L.; Wong, D.W.; Liu, J.; Wong, T.Y(2017). Detecting retinal microaneurysms and hemorrhages with robustness to the presence of blood vessels. Comput. Methods Programs Biomed.
  6. Seoud, L.; Chelbi, J.; Cheriet, F(2015). Automatic grading of diabetic retinopathy on a public database.

Cite this essay

Fundus Image Classification. (2019, Dec 16). Retrieved from https://studymoose.com/fundus-image-classification-essay

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