Cotton-wool spots, red-lesions and hard-exudates distinction using CNN enhancement and transfer learning
The automatic retinal disease diagnosis by artificial intelligent is an interesting and challenging topic in the medical field. It requires an appropriate image enhancement technique and a sufficient training dataset for the specific retina conditions. The aim of this study was to design an automati...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Other Authors: | |
| Format: | EJournal Article |
| Published: |
Institute of Advanced Engineering and Science,
2021-08-01.
|
| Subjects: | |
| Online Access: | Get fulltext |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| LEADER | 02587 am a22003493u 4500 | ||
|---|---|---|---|
| 001 | ijeecs25144_15311 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Tan, Tian-Swee |e author |
| 100 | 1 | 0 | |a Universiti Teknologi Malaysia and Fundamental Research Grant Scheme |q (FRGS) |e contributor |
| 700 | 1 | 0 | |a As'ari, M. A. |e author |
| 700 | 1 | 0 | |a Wan Hitam, Wan Hazabbah |e author |
| 700 | 1 | 0 | |a Ngoo, Qi Zhe |e author |
| 700 | 1 | 0 | |a Foh thye, Matthias Tiong |e author |
| 700 | 1 | 0 | |a Chia hiik, Kelvin Ling |e author |
| 245 | 0 | 0 | |a Cotton-wool spots, red-lesions and hard-exudates distinction using CNN enhancement and transfer learning |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-08-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25144 | ||
| 520 | |a The automatic retinal disease diagnosis by artificial intelligent is an interesting and challenging topic in the medical field. It requires an appropriate image enhancement technique and a sufficient training dataset for the specific retina conditions. The aim of this study was to design an automatic diagnosis convolutional neural network (CNN) model which does not require a large training dataset to specifically identify diabetic retinopathy symptoms, which are cotton wool, exudates spots and red lesionin colour fundus pictures. A novel framework comprised image enhancement method by using upgraded contrast limited adaptive histogram equalization (UCLAHE) filter and transferred pre-trained networks was developed to classify the retinal diseases regarding to the symptoms. The performance of the proposed framework was evaluated based on accuracy, sensitivity and specificity metrics. The collected results have proven the robustness of the proposed framework in offering good accuracy in retina diseases diagnosis. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Accurracy; CLAHE filter; CNN; Image enhancement; Retinal disease | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 23, No 2: August 2021; 1170-1179 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v23.i2 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25144/15311 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/25144/15311 |z Get fulltext |