Visualizing the Indicators of Diabetic Retinopathy Learnt by Convolutional Neural Networks

Shikhar Srivastava; Srikanth Prabhu; Sidharth Ramesh; Siddarth Pratapneni; Ashwin Abraham; Sulatha V Bhandary

doi:10.1109/ICCIC.2017.8524578

Visualizing the Indicators of Diabetic Retinopathy Learnt by Convolutional Neural Networks

Shikhar Srivastava
, Srikanth Prabhu
, Sidharth Ramesh
, Siddarth Pratapneni
, Ashwin Abraham
, Sulatha V Bhandary

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Citations (Scopus)

Abstract

This study proposes a novel application of visualizing features learnt by convolutional neural networks with the aim to further the understanding of Diabetic Retinopathy. A convolutional neural network is first trained to recognize and classify fundus images of diabetic and non-diabetic patients. The network is then visualized, using a technique of pixel optimization, to discover the features that the trained network looks for to classify the image. Through this novel application of network visualization, we show that critical features for diabetic retinopathy can be re-discovered, leaving great scope for its application in scarcely explored diseases using minimal resources.

Original language	English
Title of host publication	2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017
Editors	N. Krishnan, M. Karthikeyan
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509066209
DOIs	https://doi.org/10.1109/ICCIC.2017.8524578
Publication status	Published - 05-11-2018
Event	8th IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017 - Tamilnadu, India Duration: 14-12-2017 → 16-12-2017

Publication series

Name	2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017

Conference

Conference	8th IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017
Country/Territory	India
City	Tamilnadu
Period	14-12-17 → 16-12-17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Signal Processing
Artificial Intelligence
Computational Theory and Mathematics
Computer Science Applications

Access to Document

10.1109/ICCIC.2017.8524578

Cite this

Srivastava, S., Prabhu, S., Ramesh, S., Pratapneni, S., Abraham, A., & V Bhandary, S. (2018). Visualizing the Indicators of Diabetic Retinopathy Learnt by Convolutional Neural Networks. In N. Krishnan, & M. Karthikeyan (Eds.), 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017 Article 8524578 (2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCIC.2017.8524578

Srivastava, Shikhar ; Prabhu, Srikanth ; Ramesh, Sidharth et al. / Visualizing the Indicators of Diabetic Retinopathy Learnt by Convolutional Neural Networks. 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017. editor / N. Krishnan ; M. Karthikeyan. Institute of Electrical and Electronics Engineers Inc., 2018. (2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017).

@inproceedings{8021c268701049ddb9d843ab9c40a64f,

title = "Visualizing the Indicators of Diabetic Retinopathy Learnt by Convolutional Neural Networks",

abstract = "This study proposes a novel application of visualizing features learnt by convolutional neural networks with the aim to further the understanding of Diabetic Retinopathy. A convolutional neural network is first trained to recognize and classify fundus images of diabetic and non-diabetic patients. The network is then visualized, using a technique of pixel optimization, to discover the features that the trained network looks for to classify the image. Through this novel application of network visualization, we show that critical features for diabetic retinopathy can be re-discovered, leaving great scope for its application in scarcely explored diseases using minimal resources.",

author = "Shikhar Srivastava and Srikanth Prabhu and Sidharth Ramesh and Siddarth Pratapneni and Ashwin Abraham and \{V Bhandary\}, Sulatha",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 8th IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017 ; Conference date: 14-12-2017 Through 16-12-2017",

year = "2018",

month = nov,

day = "5",

doi = "10.1109/ICCIC.2017.8524578",

language = "English",

series = "2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

editor = "N. Krishnan and M. Karthikeyan",

booktitle = "2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017",

address = "United States",

}

Srivastava, S, Prabhu, S, Ramesh, S, Pratapneni, S, Abraham, A & V Bhandary, S 2018, Visualizing the Indicators of Diabetic Retinopathy Learnt by Convolutional Neural Networks. in N Krishnan & M Karthikeyan (eds), 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017., 8524578, 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017, Institute of Electrical and Electronics Engineers Inc., 8th IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017, Tamilnadu, India, 14-12-17. https://doi.org/10.1109/ICCIC.2017.8524578

Visualizing the Indicators of Diabetic Retinopathy Learnt by Convolutional Neural Networks. / Srivastava, Shikhar; Prabhu, Srikanth; Ramesh, Sidharth et al.
2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017. ed. / N. Krishnan; M. Karthikeyan. Institute of Electrical and Electronics Engineers Inc., 2018. 8524578 (2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Visualizing the Indicators of Diabetic Retinopathy Learnt by Convolutional Neural Networks

AU - Srivastava, Shikhar

AU - Prabhu, Srikanth

AU - Ramesh, Sidharth

AU - Pratapneni, Siddarth

AU - Abraham, Ashwin

AU - V Bhandary, Sulatha

PY - 2018/11/5

Y1 - 2018/11/5

N2 - This study proposes a novel application of visualizing features learnt by convolutional neural networks with the aim to further the understanding of Diabetic Retinopathy. A convolutional neural network is first trained to recognize and classify fundus images of diabetic and non-diabetic patients. The network is then visualized, using a technique of pixel optimization, to discover the features that the trained network looks for to classify the image. Through this novel application of network visualization, we show that critical features for diabetic retinopathy can be re-discovered, leaving great scope for its application in scarcely explored diseases using minimal resources.

AB - This study proposes a novel application of visualizing features learnt by convolutional neural networks with the aim to further the understanding of Diabetic Retinopathy. A convolutional neural network is first trained to recognize and classify fundus images of diabetic and non-diabetic patients. The network is then visualized, using a technique of pixel optimization, to discover the features that the trained network looks for to classify the image. Through this novel application of network visualization, we show that critical features for diabetic retinopathy can be re-discovered, leaving great scope for its application in scarcely explored diseases using minimal resources.

UR - https://www.scopus.com/pages/publications/85057964991

UR - https://www.scopus.com/pages/publications/85057964991#tab=citedBy

U2 - 10.1109/ICCIC.2017.8524578

DO - 10.1109/ICCIC.2017.8524578

M3 - Conference contribution

AN - SCOPUS:85057964991

T3 - 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017

BT - 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017

A2 - Krishnan, N.

A2 - Karthikeyan, M.

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 8th IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017

Y2 - 14 December 2017 through 16 December 2017

ER -

Srivastava S, Prabhu S, Ramesh S, Pratapneni S, Abraham A, V Bhandary S. Visualizing the Indicators of Diabetic Retinopathy Learnt by Convolutional Neural Networks. In Krishnan N, Karthikeyan M, editors, 2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017. Institute of Electrical and Electronics Engineers Inc. 2018. 8524578. (2017 IEEE International Conference on Computational Intelligence and Computing Research, ICCIC 2017). doi: 10.1109/ICCIC.2017.8524578

Visualizing the Indicators of Diabetic Retinopathy Learnt by Convolutional Neural Networks

Abstract

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