DETECTION OF DIABETIC RETINOPATHY USING RETINAL FUNDUS PHOTOGRAPHS BASED ON DEEP LEARNING ALGORITHM.

In this modern world, diagnosis of disease by using advanced technology plays a vital role in the medical field. Medical imaging techniques are used to detect diseases by visualizing the image. A better way to detect the disease is using Deep learning (DL) algorithm. DL is the subset of artificial i...

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Publicado en:Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 2040 - 2047
Autores principales: A., KAVITHA, S., ABINAYA, K., PRAMITHA, K., VAITHEESWARI
Formato: pictorial tables/charts Journal Article
Publicado: Turkish Journal of Physiotherapy & Rehabilitation 2021
Acceso en línea:Ver este registro en EBSCOhost
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      dt: 2021
      vid: 32
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      pub: Turkish Journal of Physiotherapy & Rehabilitation
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      tig:
        atl: DETECTION OF DIABETIC RETINOPATHY USING RETINAL FUNDUS PHOTOGRAPHS BASED ON DEEP LEARNING ALGORITHM.
      aug:
        au:
          A., KAVITHA
          S., ABINAYA
          K., PRAMITHA
          K., VAITHEESWARI
        affil: Professor, Department of Electronics and Communication Engineering M. Kumarsamy College of Engineering, Thalavapalayam, Karur, Tamil Nadu
      sug:
        subj:
          Diabetic Retinopathy Diagnosis
          Deep Learning
          Algorithms
          Neural Networks (Computer)
          Artificial Intelligence
          Photography
          Blood Glucose
          Retina
          Blindness
          Diagnostic Imaging
      ab: In this modern world, diagnosis of disease by using advanced technology plays a vital role in the medical field. Medical imaging techniques are used to detect diseases by visualizing the image. A better way to detect the disease is using Deep learning (DL) algorithm. DL is the subset of artificial intelligence (AI) which thus a subset of computerized reasoning(AI). In deep learning technique, the features are picked out by the neural network without human intervention. Diabetic retinopathy is caused by high sugar level in the blood which damages the back part of the eye called retina. Ineffectively controlled glucose is one of the danger factors to cause diabetic retinopathy. Early symptoms include floaters, blurriness, micro-aneurysms and difficulty in perceiving colours. In severe cases, blindness can occur. Due to this reason, the early detection of retinopathy is of critical importance. The retinal fundus photographs will be processed with the help of a deep learning algorithm called "Convolutional Neural Network" which helps in classifying the image by learning distinctive features of the retinal image by itself. Diabetic retinopathy can be recognized a lot quicker and more exact outcomes will be normal with the assistance of CNN calculation. Since, it is more efficient by identifying the features accurately as well as its relation with other features in an image. This paper proposes the solution for diabetic retinopathy detection in eyes using deep learning.
      pubtype: Academic Journal
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        pictorial
        tables/charts
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      ougenre: Article
    language: English
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