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DEC-DRR: deep ensemble of classification model for diabetic retinopathy recognition

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Abstract

Most diabetes patients are liable to have diabetic retinopathy (DR); however, the majority of them might not be even aware of the ailment. Therefore, early detection and treatment of DR are necessary to prevent vision loss. But, avoiding DR is not a simple process. An ophthalmologist can typically identify DR through an optical evaluation of the fundus and through the evaluation of color pictures. However, due to the increased count of DR patients, this could not be possible as it consumes more time. To rectify this problem, a novel deep ensemble-based DR classification technique is developed in this work. Initially, a Wiener filter (WF) is applied for preprocessing the image. Then, the enhanced U-Net-based segmentation process is done. Subsequent to the segmentation process, features are extracted that include statistical features, inferior superior nasal temporal (ISNT), cup to disc ratio (CDR), and improved LGBP as well. Further, deep ensemble classifiers (DEC) like CNN, Bi-GRU, and DMN are used to recognize the disease. The outcomes from DMN, CNN, and Bi-GRU are then subjected to improved SLF. Additionally, the weights of DMN, CNN, and Bi-GRU are adjusted via pelican updated Tasmanian devil optimization (PU-TDO). Finally, outputs on DR (microaneurysms, hemorrhages, hard exudates, and soft exudates) are obtained. The performance of DEC + PU-TDO for diabetic retinopathy is computed over extant models with regard to different measures for four datasets. The results on accuracy using the DEC + PU-TDO scheme for the IDRID dataset is maximum around 0.975 at 90th LP while other models have less accuracy. The FPR of DEC + PU-TDO is less around 0.039 at the 90th LP for the SUSTech-SYSU dataset, while other extant models have maximum FPR.

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Data availability

The data gathered for this research is accessible in https://rrc.cvc.uab.es/?ch=3&com=downloads.

Abbreviations

ANN:

Artificial neural network

AHE:

Adaptive histogram equalization

AOA:

Arithmetic optimization algorithm

BCE:

Binary cross entropy

Bi-GRU:

Bidirectional gated recurrent unit

CLAHE:

Contrast limited adaptive histogram equalization

CNN:

Convolutional NN

CDR:

Cup to disc ratio

CMBO:

Cat and mouse-based optimizer

DNN:

Deep neural network

DCNNs:

Deep convolutional neural networks

DR:

Diabetic retinopathy

DL:

Deep learning

DEC:

Deep ensemble classifiers

DMN:

Deep max network

e2e:

End-to-end

HIMLA:

Hybrid inductive machine learning algorithm

HGS:

Hunger games search

HE:

Histogram equalization

IRes-V2 DNN:

Inceptionresnet-V2 deep neural network

FCM:

Fuzzy means clustering

GAM:

Gated attention mechanism

ISNT:

Inferior superior nasal temporal

ML:

Machine learning

LGBP:

Local Gabor binary pattern

TL:

Transfer learning

LP:

Learning percentage

LBP:

Local binary pattern

POA:

Pelican optimization

PU-TDO:

Pelican updated Tasmanian devil optimization

NN:

Neural networks

SD:

Standard deviation

SSO:

Simplified swarm optimization

SSOA:

Shuffled shepherd optimization algorithm

SVM:

Support vector machine

SLF:

Score level fusion

TDO:

Tasmanian devil optimization

RNN:

Recurrent neural network

WF:

Wiener filter

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Marthandam College of Engineering and Technology, Kuttakuzhi, Veeyannoor, Kanyakumari, Tamil Nadu, India

    L.B. Lisha

  2. Department of Electronics and Communication Engineering, St. Xavier’s Catholic College of Engineering, Chunkankadai, Kanyakumari, Tamil Nadu, India

    C. Helen Sulochana

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Lisha, L., Helen Sulochana, C. DEC-DRR: deep ensemble of classification model for diabetic retinopathy recognition. Med Biol Eng Comput (2024). https://doi.org/10.1007/s11517-024-03076-1

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