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Understanding CNN's Decision Making on OCT-based AMD Detection
Computational Color and Spectral Image Analysis Lab, Computer Science and Engineering, Discipline Khulna University, Khulna, Bangladesh.
Computational Color and Spectral Image Analysis Lab, Computer Science and Engineering, Discipline Khulna University, Khulna, Bangladesh. (MPI, AASS)ORCID iD: 0000-0001-7387-6650
Australian e-health Research Centre, CSIRO, Perth, Australia.
2021 (English)In: 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), 14-16 Sept. 2021, IEEE, 2021, p. 1-4Conference paper, Published paper (Refereed)
Abstract [en]

Age-related Macular degeneration (AMD) is the third leading cause of incurable acute central vision loss. Optical coherence tomography (OCT) is a diagnostic process used for both AMD and diabetic macular edema (DME) detection. Spectral-domain OCT (SD-OCT), an improvement of traditional OCT, has revolutionized assessing AMD for its high acquiring rate, high efficiency, and resolution. To detect AMD from normal OCT scans many techniques have been adopted. Automatic detection of AMD has become popular recently. The use of a deep Convolutional Neural Network (CNN) has helped its cause vastly. Despite having achieved better performance, CNN models are often criticized for not giving any justification in decision-making. In this paper, we aim to visualize and critically analyze the decision of CNNs in context-based AMD detection. Multiple experiments were done using the DUKE OCT dataset, utilizing transfer learning in Resnet50 and Vgg16 model. After training the model for AMD detection, Gradient-weighted Class Activation Mapping (Grad-Cam) is used for feature visualization. With the feature mapped image, each layer mask was compared. We have found out that the Outer Nuclear layer to the Inner segment myeloid (ONL-ISM) has more predominance about 17.13% for normal and 6.64% for AMD in decision making.

Place, publisher, year, edition, pages
IEEE, 2021. p. 1-4
Keywords [en]
AMD, OCT, CNN, macula, retina, Grad-Cam, visualization
National Category
Computer Vision and Robotics (Autonomous Systems) Computer Sciences
Research subject
Computerized Image Analysis; Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-96707DOI: 10.1109/ICECIT54077.2021.9641246ISI: 000855845700044ISBN: 9781665423632 (electronic)ISBN: 9781665423649 (print)OAI: oai:DiVA.org:oru-96707DiVA, id: diva2:1632412
Conference
2021 International Conference on Electronics, Communications and Information Technology (ICECIT), Khulna, Bangladesh, September 14-16, 2021
Available from: 2022-01-26 Created: 2022-01-26 Last updated: 2022-11-25Bibliographically approved

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Rahaman, G. M. Atiqur

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