PMID- 33439453
OWN - NLM
STAT- MEDLINE
DCOM- 20210929
LR  - 20210929
IS  - 1741-0444 (Electronic)
IS  - 0140-0118 (Linking)
VI  - 59
IP  - 2
DP  - 2021 Feb
TI  - An enhanced deep image model for glaucoma diagnosis using feature-based detection 
      in retinal fundus.
PG  - 333-353
LID - 10.1007/s11517-020-02307-5 [doi]
AB  - This paper proposes a deep image analysis-based model for glaucoma diagnosis that 
      uses several features to detect the formation of glaucoma in retinal fundus. 
      These features are combined with most extracted parameters like inferior, 
      superior, nasal, and temporal region area, and cup-to-disc ratio that overall 
      forms a deep image analysis. This proposed model is exercised to investigate the 
      various aspects related to the prediction of glaucoma in retinal fundus images 
      that help the ophthalmologist in making better decisions for the human eye. The 
      proposed model is presented with the combination of four machine learning 
      algorithms that provide the classification accuracy of 98.60% while other 
      existing models like support vector machine (SVM), K-nearest neighbors (KNN), and 
      Naive Bayes provide individually with accuracies of 97.61%, 90.47%, and 95.23% 
      respectively. These results clearly demonstrate that this proposed model offers 
      the best methodology to an early diagnosis of glaucoma in retinal fundus.
FAU - Singh, Law Kumar
AU  - Singh LK
AD  - Department of Computer Science and Engineering, School of Engineering and 
      Technology, Sharda University, Knowledge Park III, Greater Noida, India.
AD  - Department of Computer Science and Engineering, Hindustan College of Science and 
      Technology, Mathura, India.
FAU - Pooja
AU  - Pooja
AD  - Department of Computer Science and Engineering, School of Engineering and 
      Technology, Sharda University, Knowledge Park III, Greater Noida, India.
FAU - Garg, Hitendra
AU  - Garg H
AD  - Department of Computer Engineering and Applications, GLA University, Mathura, 
      India.
FAU - Khanna, Munish
AU  - Khanna M
AD  - Department of Computer Science and Engineering, Hindustan College of Science and 
      Technology, Mathura, India.
FAU - Bhadoria, Robin Singh
AU  - Bhadoria RS
AUID- ORCID: 0000-0002-6314-4736
AD  - Department of Computer Science and Engineering, Birla Institute of Applied 
      Sciences (BIAS), Bhimtal, Uttarakhand, India. robin19@ieee.org.
LA  - eng
PT  - Journal Article
DEP - 20210113
PL  - United States
TA  - Med Biol Eng Comput
JT  - Medical & biological engineering & computing
JID - 7704869
SB  - IM
MH  - Algorithms
MH  - Bayes Theorem
MH  - Fundus Oculi
MH  - *Glaucoma/diagnosis
MH  - Humans
MH  - Image Interpretation, Computer-Assisted
OTO - NOTNLM
OT  - Cup-to-disc ratio
OT  - Glaucoma diagnosis
OT  - Inferior superior nasal temporal (ISNT) regions
OT  - Machine learning
OT  - Retinal fundus image
EDAT- 2021/01/14 06:00
MHDA- 2021/09/30 06:00
CRDT- 2021/01/13 12:14
PHST- 2020/05/25 00:00 [received]
PHST- 2020/12/26 00:00 [accepted]
PHST- 2021/01/14 06:00 [pubmed]
PHST- 2021/09/30 06:00 [medline]
PHST- 2021/01/13 12:14 [entrez]
AID - 10.1007/s11517-020-02307-5 [pii]
AID - 10.1007/s11517-020-02307-5 [doi]
PST - ppublish
SO  - Med Biol Eng Comput. 2021 Feb;59(2):333-353. doi: 10.1007/s11517-020-02307-5. 
      Epub 2021 Jan 13.