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Optimal feature selection-based diabetic retinopathy detection using improved rider optimization algorithm enabled with deep learning

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Abstract

This proposal tempts to develop automated DR detection by analyzing the retinal abnormalities like hard exudates, haemorrhages, Microaneurysm, and soft exudates. The main processing phases of the developed DR detection model is Pre-processing, Optic Disk removal, Blood vessel removal, Segmentation of abnormalities, Feature extraction, Optimal feature selection, and Classification. At first, the pre-processing of the input retinal image is done by Contrast Limited Adaptive Histogram Equalization. The next phase performs the optic disc removal, which is carried out by open-close watershed transformation. Further, the Grey Level thresholding is done for segmenting the blood vessels and its removal. Once the optic disk and blood vessels are removed, segmentation of abnormalities is done by Top hat transformation and Gabor filtering. Further, the feature extraction phase is started, which tends to extract four sets of features like Local Binary Pattern, Texture Energy Measurement, Shanon’s and Kapur’s entropy. Since the length of the feature vector seems to be long, the feature selection process is done, which selects the unique features with less correlation. Moreover, the Deep Belief Network (DBN)-based classification algorithm performs the categorization of images into four classes normal, earlier, moderate, or severe stages. The optimal feature selection is done by the improved meta-heuristic algorithm called Modified Gear and Steering-based Rider Optimization Algorithm (MGS-ROA), and the same algorithm updates the weight in DBN. Finally, the effectual performance and comparative analysis prove the stable and reliable performance of the proposed model over existing models. The performance of the proposed model is compared with the existing classifiers, such as, NN, KNN, SVM, DBN and the conventional Heuristic-Based DBNs, such as PSO-DBN, GWO-DBN, WOA-DBN, and ROA-DBN for the evaluation metrics, accuracy, sensitivity, specificity, precision, FPR, FNR, NPV, FDR, F1 score, and MC. From the results, it is exposed that the accuracy of the proposed MGS-ROA-DBN is 30.1% higher than NN, 32.2% higher than KNN, and 17.1% higher than SVM and DBN. Similarly, the accuracy of the developed MGS-ROA-DBN is 13.8% superior to PSO, 5.1% superior to GWO, 10.8% superior to WOA, and 2.5% superior to ROA.

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Abbreviations

IRMA:

Intra retinal microvascular abnormalities

ROA:

Rider optimization algorithm

DBN:

Deep belief network

DR:

Diabetic retinopathy

MGS-ROA:

Modified gear and steering-based ROA

ETDRS:

Early treatment DR study

NPDR:

Non-proliferative DR

FPR:

False positive rate

PDR:

Proliferative DR

HOG:

Histogram of oriented gradients

SIFT:

Scale invariant feature transform

FNR:

False negative rate

LBP:

Local binary pattern

Deep CNN:

Deep convolutional neural network

SASG:

Single annotations by single grader

NPV:

Negative predictive value

SAMG:

Single annotations from multiple graders

MAV:

Multiple annotations by voting

FDR:

False discovery rate

DAAD:

Double annotations with adjudication of disagreement

ANN:

Artificial neural network

PSO:

Particle swarm optimization

MCC:

Mathews correlation coefficient

MIL:

Multiple instance learning

PCA:

Principal component analysis

GWO:

Grey wolf optimization

WOA:

Whale optimization algorithm

CLAHE:

Contrast limited adaptive histogram equalization

CNN:

Convolutional neural network

NN:

Neural network

TEM:

Texture energy measurement

CPD:

Cumulative probability distribution

SKIZ:

Skeleton of influence zones

SVM:

Support vector machine

LTE:

Laws texture energy

KNN:

K-nearest neighbour

RBM:

Restricted Boltzmann machine

CD:

Contrastive divergence

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Author information

Authors and Affiliations

  1. Department of Electrical and Electronics, B.L.D.E.A’s V.P. Dr. P.G. Halakatti College of Engineering and Technology, (Affiliated to Visvesvaraya Technological University, Belagavi-590018), Vijayapur, Karnataka, 586103, India

    Ambaji S. Jadhav

  2. Department of Computer Science and Engineering, B.L.D.E.A’s V.P. Dr. P.G. Halakatti College of Engineering and Technology, (Affiliated to Visvesvaraya Technological University, Belagavi-590018), Vijayapur, Karnataka, 586103, India

    Pushpa B. Patil

  3. Ophthalmology, B.L.D.E.A’s Medical College and Research Center, Vijayapur, India

    Sunil Biradar

Authors
  1. Ambaji S. Jadhav
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  2. Pushpa B. Patil
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  3. Sunil Biradar
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Correspondence to Ambaji S. Jadhav.

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Jadhav, A.S., Patil, P.B. & Biradar, S. Optimal feature selection-based diabetic retinopathy detection using improved rider optimization algorithm enabled with deep learning. Evol. Intel. 14, 1431–1448 (2021). https://doi.org/10.1007/s12065-020-00400-0

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  • DOI: https://doi.org/10.1007/s12065-020-00400-0

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