Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model

Table 1

Deep learning models.

No.	DL model	Description	Remark
1.	CNN	It consists of a set of fully connected layers and convolution layers	Require a few input parameters
2.	DarkNet	Classification model for object detection	Used for real-time object detection
3.	DNN	It has many hidden nodes compared with the conventional neural network	Performs deep nonlinear analysis
4.	GoogleNet	It is an improved DL model for image analysis	Used for object detection with a few input parameters
5.	InceptionResNetV2	It has a fixable architecture of a CNN	Used for different types of applications
6.	Inceptionv3	Third generation of Google's Inception CNN	Used for classifying visual objects for computer vision applications
7.	LSTM	A type of recurrent neural network (RNN)	Used for dealing with sequences of data
8.	MobileNetV2	A lower complexity and model size DL neural network proposed by Google for mobile phone image processing applications	Used for object detection, classification, and semantic segmentation
9.	NASNet-Large	A CNN modeled to deal with a large scale of image datasets.	Used to classify objects
10.	ResNet34	A CNN architecture but with shortcuts and bottleneck block mechanisms between layers to speed up solving problems.	Used for deep real-time analysis
11.	ResNet50	A type of CNN that performs deeper analysis to solve complex problems	The deeper analysis might degrade the accuracy of the network
12.	SAE	A multilayer neural network with a stacked autoencoder	Used for datasets with a small dimension of features.
13.	VGG16	A CNN with multiple 3 × 3 kernel-sized filters in the convolutional layers	Used for recognition tasks of a large-scale number of images dataset
14.	VGG19	A CNN with multiple 3 × 3 kernel-sized filters in the convolutional layers with additional layers than the VGG16	Used for recognition tasks of a large-scale number of images dataset
15.	Xception	An improved version of the Inception family of CNN	Used for classifying visual objects for computer vision applications with a slightly higher accuracy