Hyper-Tuned CNN Using EVO Technique for Efficient Biomedical Image Classification

Table 1

Summary of the state of art models.

Ref.	Model used	Advantage	Disadvantage
[1]	Hyper-tuned CNN using spotted hyena optimizer (SHO)	Improved accuracy 99.25%	Hyperparameter tuning model is considered complex
[2]	CNN and SVM	Accuracy 98.49%.	High computation complexity
[3]	Feedforward back-propagation artificial neural network (FP-ANN) and k-nearest neighbour (k-NN)	FP-ANN and k-NN are 97% and 98% respectively	No hyperparameters optimization
[14]	Hyper-tuned CNN using univariate dynamic encoding (UDE) algorithm.	Above 95% accuracy.	High implementation requirements
[15]	CNN optimized hyperparameters based on genetic algorithm	90% accuracy	Low accuracy and high computational complexity
[16]	Hyper-tuned parameters of (CNN) using multi-level particle swarm optimization (MPSO).	Above 95% accuracy	The complexity and performance depend on the dimensions of the search space.
[20]	Deep convolutional neural network (DCNN).	97.72% accuracy	High computation complexity
Wang et al. [18]	CNN with adaptive momentum optimization	95% accuracy	Low accuracy and high computational complexity