Application of Deep Learning in Early Diagnosis Assistant System of Keratoconus.
10.3969/j.issn.1671-7104.2019.02.002
- Author:
Anzu TAN
1
;
Man YU
1
;
Xuan CHEN
1
;
Liang HU
1
Author Information
1. The Eye Hospital of Wenzhou Medical University, Wenzhou, 325000.
- Publication Type:Journal Article
- Keywords:
deep learning;
keratoconus;
optical coherence tomography technique
- MeSH:
Corneal Topography;
Deep Learning;
Early Diagnosis;
Humans;
Keratoconus;
diagnostic imaging;
Tomography, Optical Coherence
- From:
Chinese Journal of Medical Instrumentation
2019;43(2):83-85
- CountryChina
- Language:Chinese
-
Abstract:
In view of the problem that there is no standard diagnosis for early stage keratoconus disease,at the same time to assist the special examiner and ophthalmologist to make the early diagnosis effectively,the advantages and disadvantages of each testing instrument were analyzed.In order to construct an assistant system for early diagnosis of keratoconus,a deep learning technique was applied in corneal OCT examination.The system used improved VGG-16 to realize the recognition accuracy of about 68% keratoconus keratopathy,and the clinical results showed that the system can help doctors to give diagnosis confidence to a certain extent.At the same time,the physician's re-marking of OCT can help train the system for more accurate judgment.
