Classification of retinal diseases from OCT scans using convolutional neural networks

Suhail Najeeb, Nowshin Sharmile, Md Sajid Khan, Ipsita Sahin, Mohammad Tariqul Islam, Mohammed Imamul Hassan Bhuiyan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Biomedical image classification for diseases is a lengthy and manual process. However recent progresses in computer vision has enabled detection and classification of medical images using machine intelligence a more feasible solution. We explore the possibility of automated detection and classification of retinal abnormalities from retinal OCT scan images of patients. We develop an algorithm to detect the region of interest from a retinal OCT scan and use a computationally inexpensive single layer convolutional neutral network structure for the classification process. Our model is trained on an open source retinal OCT dataset containing 83,484 images of various tunnel disease patients and provides a feasible classification accuracy.

Original languageEnglish
Title of host publicationICECE 2018 - 10th International Conference on Electrical and Computer Engineering
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages465-468
Number of pages4
ISBN (Electronic)9781538674826
DOIs
Publication statusPublished - 6 Feb 2019
Externally publishedYes
Event10th International Conference on Electrical and Computer Engineering, ICECE 2018 - Dhaka, Bangladesh
Duration: 20 Dec 201822 Dec 2018

Publication series

NameICECE 2018 - 10th International Conference on Electrical and Computer Engineering

Conference

Conference10th International Conference on Electrical and Computer Engineering, ICECE 2018
CountryBangladesh
CityDhaka
Period20/12/1822/12/18

Fingerprint

Neural networks
image classification
intelligence
Image classification
abnormalities
computer vision
Computer vision
tunnels
Tunnels

Keywords

  • Biomedical Image Classification
  • CNN
  • Computer Vision
  • Retinal OCT Scan

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture
  • Electrical and Electronic Engineering
  • Electronic, Optical and Magnetic Materials
  • Instrumentation

Cite this

Najeeb, S., Sharmile, N., Khan, M. S., Sahin, I., Islam, M. T., & Hassan Bhuiyan, M. I. (2019). Classification of retinal diseases from OCT scans using convolutional neural networks. In ICECE 2018 - 10th International Conference on Electrical and Computer Engineering (pp. 465-468). [8636699] (ICECE 2018 - 10th International Conference on Electrical and Computer Engineering). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECE.2018.8636699

Classification of retinal diseases from OCT scans using convolutional neural networks. / Najeeb, Suhail; Sharmile, Nowshin; Khan, Md Sajid; Sahin, Ipsita; Islam, Mohammad Tariqul; Hassan Bhuiyan, Mohammed Imamul.

ICECE 2018 - 10th International Conference on Electrical and Computer Engineering. Institute of Electrical and Electronics Engineers Inc., 2019. p. 465-468 8636699 (ICECE 2018 - 10th International Conference on Electrical and Computer Engineering).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Najeeb, S, Sharmile, N, Khan, MS, Sahin, I, Islam, MT & Hassan Bhuiyan, MI 2019, Classification of retinal diseases from OCT scans using convolutional neural networks. in ICECE 2018 - 10th International Conference on Electrical and Computer Engineering., 8636699, ICECE 2018 - 10th International Conference on Electrical and Computer Engineering, Institute of Electrical and Electronics Engineers Inc., pp. 465-468, 10th International Conference on Electrical and Computer Engineering, ICECE 2018, Dhaka, Bangladesh, 20/12/18. https://doi.org/10.1109/ICECE.2018.8636699
Najeeb S, Sharmile N, Khan MS, Sahin I, Islam MT, Hassan Bhuiyan MI. Classification of retinal diseases from OCT scans using convolutional neural networks. In ICECE 2018 - 10th International Conference on Electrical and Computer Engineering. Institute of Electrical and Electronics Engineers Inc. 2019. p. 465-468. 8636699. (ICECE 2018 - 10th International Conference on Electrical and Computer Engineering). https://doi.org/10.1109/ICECE.2018.8636699
Najeeb, Suhail ; Sharmile, Nowshin ; Khan, Md Sajid ; Sahin, Ipsita ; Islam, Mohammad Tariqul ; Hassan Bhuiyan, Mohammed Imamul. / Classification of retinal diseases from OCT scans using convolutional neural networks. ICECE 2018 - 10th International Conference on Electrical and Computer Engineering. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 465-468 (ICECE 2018 - 10th International Conference on Electrical and Computer Engineering).
@inproceedings{ac2e688e51a24f11a17b94d7dd070124,
title = "Classification of retinal diseases from OCT scans using convolutional neural networks",
abstract = "Biomedical image classification for diseases is a lengthy and manual process. However recent progresses in computer vision has enabled detection and classification of medical images using machine intelligence a more feasible solution. We explore the possibility of automated detection and classification of retinal abnormalities from retinal OCT scan images of patients. We develop an algorithm to detect the region of interest from a retinal OCT scan and use a computationally inexpensive single layer convolutional neutral network structure for the classification process. Our model is trained on an open source retinal OCT dataset containing 83,484 images of various tunnel disease patients and provides a feasible classification accuracy.",
keywords = "Biomedical Image Classification, CNN, Computer Vision, Retinal OCT Scan",
author = "Suhail Najeeb and Nowshin Sharmile and Khan, {Md Sajid} and Ipsita Sahin and Islam, {Mohammad Tariqul} and {Hassan Bhuiyan}, {Mohammed Imamul}",
year = "2019",
month = "2",
day = "6",
doi = "10.1109/ICECE.2018.8636699",
language = "English",
series = "ICECE 2018 - 10th International Conference on Electrical and Computer Engineering",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "465--468",
booktitle = "ICECE 2018 - 10th International Conference on Electrical and Computer Engineering",
address = "United States",

}

TY - GEN

T1 - Classification of retinal diseases from OCT scans using convolutional neural networks

AU - Najeeb, Suhail

AU - Sharmile, Nowshin

AU - Khan, Md Sajid

AU - Sahin, Ipsita

AU - Islam, Mohammad Tariqul

AU - Hassan Bhuiyan, Mohammed Imamul

PY - 2019/2/6

Y1 - 2019/2/6

N2 - Biomedical image classification for diseases is a lengthy and manual process. However recent progresses in computer vision has enabled detection and classification of medical images using machine intelligence a more feasible solution. We explore the possibility of automated detection and classification of retinal abnormalities from retinal OCT scan images of patients. We develop an algorithm to detect the region of interest from a retinal OCT scan and use a computationally inexpensive single layer convolutional neutral network structure for the classification process. Our model is trained on an open source retinal OCT dataset containing 83,484 images of various tunnel disease patients and provides a feasible classification accuracy.

AB - Biomedical image classification for diseases is a lengthy and manual process. However recent progresses in computer vision has enabled detection and classification of medical images using machine intelligence a more feasible solution. We explore the possibility of automated detection and classification of retinal abnormalities from retinal OCT scan images of patients. We develop an algorithm to detect the region of interest from a retinal OCT scan and use a computationally inexpensive single layer convolutional neutral network structure for the classification process. Our model is trained on an open source retinal OCT dataset containing 83,484 images of various tunnel disease patients and provides a feasible classification accuracy.

KW - Biomedical Image Classification

KW - CNN

KW - Computer Vision

KW - Retinal OCT Scan

UR - http://www.scopus.com/inward/record.url?scp=85062868192&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062868192&partnerID=8YFLogxK

U2 - 10.1109/ICECE.2018.8636699

DO - 10.1109/ICECE.2018.8636699

M3 - Conference contribution

T3 - ICECE 2018 - 10th International Conference on Electrical and Computer Engineering

SP - 465

EP - 468

BT - ICECE 2018 - 10th International Conference on Electrical and Computer Engineering

PB - Institute of Electrical and Electronics Engineers Inc.

ER -