Enhancement of Detection of Diabetic Retinopathy Using Harris Hawks Optimization with Deep Learning Model

Table 1

Review of deep learning applications in diabetic retinopathy and other datasets.

Reference	Dataset	Method used	Evaluation metrics	Research challenges
[19]	Diabetic retinopathy (DR) dataset consisted of 75137 images	5-Fold cross-validation and data-driven deep learning algorithm	Sensitivity, specificity, and AUC score	The results were not properly evaluated using typical state-of-the-art models
[20]	73 patients (122 eyes) were evaluated, 50.7% men and 49.3% women	RBM-1000, RBM-500, and OPF-1000	Sensitivity measured, specificity, and accuracy	More in-depth analysis on larger datasets was missing and accuracy may also be improved
[21]	14,186 retinal images and Messidor dataset with 1200 images	Deep learning algorithm	Accuracy, sensitivity, specificity, positive and negative predictive values, and AUC	Dataset is fixed and is not compared with other technique
[22]	128175 retinal images, EyePACS-1 dataset consisted of 9963 images, and Messidor-2 dataset with 1748 images	Deep convolutional neural network	The algorithm had 97.5% and 96.1% sensitivity and 93.4% and 93.9% specificity in the 2 validation sets	Limited dataset, system maybe failed to learn more complex features
[23]	Heart disease dataset	Effective heart disease prediction system using enhanced deep genetic algorithm and adaptive Harris hawks optimization-based clustering	Accuracy, precision, recall, specificity, and F-score	Requires more improvement in the learning process
[24]	COVID-CT-dataset: 349 and 397 images and CT scans for COVID-19 classification: 4,001 and 9,979 images	Hybrid learning and optimization approach CovH2SD-CovH2SD uses DL. HHO algorithm to optimize the hyperparameters	Accuracy, precision, recall, F1-score, and AUC performance metrics	Not good for multiclass classification
[25]	Hand gesture dataset from Kaggle repository	HHO is used for hyperparameter tuning of CNN for enhancing hand gesture recognition	Reduction of the burden on the CNN by reducing the training time and 100% accuracy for hand gesture classification is attained	Requires more improvement in the learning process