PMID- 37368025
OWN - NLM
STAT- MEDLINE
DCOM- 20230823
LR  - 20230823
IS  - 1432-5233 (Electronic)
IS  - 0940-5429 (Linking)
VI  - 60
IP  - 10
DP  - 2023 Oct
TI  - Convolutional neural network-based sea lion optimization algorithm for the 
      detection and classification of diabetic retinopathy.
PG  - 1377-1389
LID - 10.1007/s00592-023-02122-y [doi]
AB  - AIMS: Diabetic retinopathy (DR) becomes a complicated type of diabetic that 
      causes damage to the blood vessels of the retina's light-sensitive tissue. DR may 
      initially cause mild symptoms or no symptoms. But prolonged DR results in 
      permanent vision loss, and hence, it is necessary to detect the DR at an early 
      stage. METHODS: Manual diagnosing of DR retina fundus image is a time-consuming 
      process and sometimes leads to misdiagnosis. The existing DR detection model 
      faces few shortcomings in case of improper detection accuracy, higher loss or 
      error values, high feature dimensionality, not suitable for large datasets, high 
      computational complexity, poor performances, unbalanced and limited number of 
      data points, and so on. As a result, the DR is diagnosed in this paper through 
      four critical phases to tackle the shortcomings. The retinal images are cropped 
      during preprocessing to reduce unwanted noises and redundant data. The images are 
      then segmented using a modified level set algorithm based on pixel 
      characteristics. RESULTS: Here, an Aquila optimizer is employed in extracting the 
      segmented image. Finally, for optimal classification of DR images, the study 
      proposes a convolutional neural network-oriented sea lion optimization (CNN-SLO) 
      algorithm. Here, the CNN-SLO algorithm classifies the retinal images into five 
      classes (healthy, moderate, mild, proliferative and severe). CONCLUSION: The 
      experimental investigation is performed for Kaggle datasets with respect to 
      diverse evaluation measures to deliberate the performances of the proposed 
      system.
CI  - (c) 2023. Springer-Verlag Italia S.r.l., part of Springer Nature.
FAU - Hemanth, S V
AU  - Hemanth SV
AUID- ORCID: 0000-0002-7829-6708
AD  - Department of Computer Science and Engineering, Kalasalingam Academy of Research 
      and Education (Deemed to be University), Srivilliputhur, Tamil Nadu, India. 
      hemanth.svace@gmail.com.
FAU - Alagarsamy, Saravanan
AU  - Alagarsamy S
AD  - Department of Computer Science and Engineering, Kalasalingam Academy of Research 
      and Education (Deemed to be University), Srivilliputhur, Tamil Nadu, India.
FAU - Dhiliphan Rajkumar, T
AU  - Dhiliphan Rajkumar T
AD  - Department of Computer Science and Engineering, Kalasalingam Academy of Research 
      and Education (Deemed to be University), Srivilliputhur, Tamil Nadu, India.
LA  - eng
PT  - Journal Article
DEP - 20230627
PL  - Germany
TA  - Acta Diabetol
JT  - Acta diabetologica
JID - 9200299
SB  - IM
MH  - Animals
MH  - *Diabetic Retinopathy/diagnosis
MH  - *Sea Lions
MH  - Neural Networks, Computer
MH  - Algorithms
MH  - Retina/diagnostic imaging
MH  - *Diabetes Mellitus
OTO - NOTNLM
OT  - Convolutional neural network and Kaggle datasets
OT  - Diabetic retinopathy
OT  - Modified level set
OT  - Sea lion optimization
EDAT- 2023/06/27 13:10
MHDA- 2023/08/23 06:42
CRDT- 2023/06/27 11:06
PHST- 2022/10/06 00:00 [received]
PHST- 2023/05/20 00:00 [accepted]
PHST- 2023/08/23 06:42 [medline]
PHST- 2023/06/27 13:10 [pubmed]
PHST- 2023/06/27 11:06 [entrez]
AID - 10.1007/s00592-023-02122-y [pii]
AID - 10.1007/s00592-023-02122-y [doi]
PST - ppublish
SO  - Acta Diabetol. 2023 Oct;60(10):1377-1389. doi: 10.1007/s00592-023-02122-y. Epub 
      2023 Jun 27.