Cite this DOI

Sultana, M. N. M., & Krishna, M. J. R. (2021). Detection of Eye Diseases (Glaucoma & ARMD). *Journal of Science & Technology*, *06*(03), 155–168. https://doi.org/10.46243/jst.2021.v6.i3.pp155-168

@article{sultana2021detection,
  author    = {Sultana, Ms. N Musrat and Krishna, Mr. Juturi Rama},
  title     = {{Detection of Eye Diseases (Glaucoma \& ARMD)}},
  journal   = {Journal of Science \& Technology},
  year      = {2021},
  month     = {jun},
  volume    = {06},
  number    = {03},
  pages     = {155--168},
  publisher = {Longman Publishers},
  issn      = {2456-5660},
  doi       = {10.46243/jst.2021.v6.i3.pp155-168},
  url       = {https://doi.org/10.46243/jst.2021.v6.i3.pp155-168},
  language  = {en},
  abstract  = {As population aging has become a major demographic trend around the world, patients suffering from eye diseases, such as Glaucoma, ARMD are expected to increase. Early detection and appropriate treatment of eye diseases are of great significance to prevent vision loss and promote living quality. Conventional diagnosis methods are tremendously dependent on physicians, professional experience and knowledge, which lead to high misdiagnosis rate and huge waste of medical data. In this project, a deep learning model-based method which is inspired by the diagnostic process of human ophthalmologists is proposed to automatically classify the fundus photographs into 2 types with or without ARMD categories also, with or without Glaucoma. The project consists of two different neural network models developed to recognize the diseases, Glaucoma and ARMD.Better accuracy is obtained as we use deep learning. This project will be an aid to eye specialists in giving an efficient treatment. Eyesight is one of the most important senses, the developed project can help people all over to maintain eye care. This project uses Kaggle Glaucoma and ARMD datasets. This model predicts Glaucoma with 90\% accuracy and ARMD with more than 70\% accuracy.}
}

TY  - JOUR
TI  - Detection of Eye Diseases (Glaucoma & ARMD)
AU  - Sultana, Ms. N Musrat
AU  - Krishna, Mr. Juturi Rama
JO  - Journal of Science & Technology
PY  - 2021
DA  - 2021/06/02/
VL  - 06
IS  - 03
SP  - 155
EP  - 168
PB  - Longman Publishers
SN  - 2456-5660
LA  - en
AB  - As population aging has become a major demographic trend around the world, patients suffering from eye diseases, such as Glaucoma, ARMD are expected to increase. Early detection and appropriate treatment of eye diseases are of great significance to prevent vision loss and promote living quality. Conventional diagnosis methods are tremendously dependent on physicians, professional experience and knowledge, which lead to high misdiagnosis rate and huge waste of medical data. In this project, a deep learning model-based method which is inspired by the diagnostic process of human ophthalmologists is proposed to automatically classify the fundus photographs into 2 types with or without ARMD categories also, with or without Glaucoma. The project consists of two different neural network models developed to recognize the diseases, Glaucoma and ARMD.Better accuracy is obtained as we use deep learning. This project will be an aid to eye specialists in giving an efficient treatment. Eyesight is one of the most important senses, the developed project can help people all over to maintain eye care. This project uses Kaggle Glaucoma and ARMD datasets. This model predicts Glaucoma with 90% accuracy and ARMD with more than 70% accuracy.
DO  - 10.46243/jst.2021.v6.i3.pp155-168
UR  - https://doi.org/10.46243/jst.2021.v6.i3.pp155-168
ER  -

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    "URL": "https://doi.org/10.46243/jst.2021.v6.i3.pp155-168",
    "title": "Detection of Eye Diseases (Glaucoma & ARMD)",
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    "abstract": "As population aging has become a major demographic trend around the world, patients suffering from eye diseases, such as Glaucoma, ARMD are expected to increase. Early detection and appropriate treatment of eye diseases are of great significance to prevent vision loss and promote living quality. Conventional diagnosis methods are tremendously dependent on physicians, professional experience and knowledge, which lead to high misdiagnosis rate and huge waste of medical data. In this project, a deep learning model-based method which is inspired by the diagnostic process of human ophthalmologists is proposed to automatically classify the fundus photographs into 2 types with or without ARMD categories also, with or without Glaucoma. The project consists of two different neural network models developed to recognize the diseases, Glaucoma and ARMD.Better accuracy is obtained as we use deep learning. This project will be an aid to eye specialists in giving an efficient treatment. Eyesight is one of the most important senses, the developed project can help people all over to maintain eye care. This project uses Kaggle Glaucoma and ARMD datasets. This model predicts Glaucoma with 90% accuracy and ARMD with more than 70% accuracy.",
    "ISSN": "2456-5660"
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From the record as registered (version 2) — the record and its history. Programs: https://registry.smartscholars.in/api.php?action=cite&doi=10.46243%2Fjst.2021.v6.i3.pp155-168 gives all four in one JSON answer.