Age-related Macular Degeneration detection using deep convolutional neural network

Jen Hong Tan, Sulatha V. Bhandary, Sobha Sivaprasad, Yuki Hagiwara, Akanksha Bagchi, U. Raghavendra, A. Krishna Rao, Biju Raju, Nitin Shridhara Shetty, Arkadiusz Gertych, Kuang Chua Chua, U. Rajendra Acharya

Research output: Contribution to journalArticle

  • 4 Citations

Abstract

Age-related Macular Degeneration (AMD) is an eye condition that affects the elderly. Further, the prevalence of AMD is rising because of the aging population in the society. Therefore, early detection is necessary to prevent vision impairment in the elderly. However, organizing a comprehensive eye screening to detect AMD in the elderly is laborious and challenging. To address this need, we have developed a fourteen-layer deep Convolutional Neural Network (CNN) model to automatically and accurately diagnose AMD at an early stage. The performance of the model was evaluated using the blindfold and ten-fold cross-validation strategies, for which the accuracy of 91.17% and 95.45% were respectively achieved. This new model can be utilized in a rapid eye screening for early detection of AMD in the elderly. It is cost-effective and highly portable, hence, it can be utilized anywhere.

LanguageEnglish
Pages127-135
Number of pages9
JournalFuture Generation Computer Systems
Volume87
DOIs
Publication statusPublished - 01-10-2018

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Neural networks
Screening
Aging of materials
Costs

All Science Journal Classification (ASJC) codes

  • Software
  • Hardware and Architecture
  • Computer Networks and Communications

Cite this

Tan, Jen Hong ; Bhandary, Sulatha V. ; Sivaprasad, Sobha ; Hagiwara, Yuki ; Bagchi, Akanksha ; Raghavendra, U. ; Krishna Rao, A. ; Raju, Biju ; Shetty, Nitin Shridhara ; Gertych, Arkadiusz ; Chua, Kuang Chua ; Acharya, U. Rajendra. / Age-related Macular Degeneration detection using deep convolutional neural network. In: Future Generation Computer Systems. 2018 ; Vol. 87. pp. 127-135.
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abstract = "Age-related Macular Degeneration (AMD) is an eye condition that affects the elderly. Further, the prevalence of AMD is rising because of the aging population in the society. Therefore, early detection is necessary to prevent vision impairment in the elderly. However, organizing a comprehensive eye screening to detect AMD in the elderly is laborious and challenging. To address this need, we have developed a fourteen-layer deep Convolutional Neural Network (CNN) model to automatically and accurately diagnose AMD at an early stage. The performance of the model was evaluated using the blindfold and ten-fold cross-validation strategies, for which the accuracy of 91.17{\%} and 95.45{\%} were respectively achieved. This new model can be utilized in a rapid eye screening for early detection of AMD in the elderly. It is cost-effective and highly portable, hence, it can be utilized anywhere.",
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Tan, JH, Bhandary, SV, Sivaprasad, S, Hagiwara, Y, Bagchi, A, Raghavendra, U, Krishna Rao, A, Raju, B, Shetty, NS, Gertych, A, Chua, KC & Acharya, UR 2018, 'Age-related Macular Degeneration detection using deep convolutional neural network' Future Generation Computer Systems, vol. 87, pp. 127-135. https://doi.org/10.1016/j.future.2018.05.001

Age-related Macular Degeneration detection using deep convolutional neural network. / Tan, Jen Hong; Bhandary, Sulatha V.; Sivaprasad, Sobha; Hagiwara, Yuki; Bagchi, Akanksha; Raghavendra, U.; Krishna Rao, A.; Raju, Biju; Shetty, Nitin Shridhara; Gertych, Arkadiusz; Chua, Kuang Chua; Acharya, U. Rajendra.

In: Future Generation Computer Systems, Vol. 87, 01.10.2018, p. 127-135.

Research output: Contribution to journalArticle

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AU - Tan, Jen Hong

AU - Bhandary, Sulatha V.

AU - Sivaprasad, Sobha

AU - Hagiwara, Yuki

AU - Bagchi, Akanksha

AU - Raghavendra, U.

AU - Krishna Rao, A.

AU - Raju, Biju

AU - Shetty, Nitin Shridhara

AU - Gertych, Arkadiusz

AU - Chua, Kuang Chua

AU - Acharya, U. Rajendra

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