Table 1.

Brief presentation of the feature extraction techniques, as well as the ML and DL models, and the main procedures that were reported in the papers of our survey.

Category	Models	Description
Feature extraction	Histogram of oriented gradient (HOG) [35]	This is a feature descriptor used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image.
	Generalized search tree (GIST) [30]	GIST descriptor represents holistic spatial scene properties (spatial envelope) of an image. It summarizes gradient information on different spatial scales and orientations by splitting the image into a grid of cells on several scales and convolving each cell using a Gabor filter bank from different perspectives.
	Gray-level co-occurrence matrix (GLCM) [36]	GLCM is a way of extracting second-order statistical texture features. In particular, the texture of an image is estimated by calculating how often pairs of pixels with specific values and a certain spatial relationship occur.
Traditional Machine Learning	k-nearest neighbor (K-NN) [37]	KNN algorithm is a simple, easy-to-implement supervised ML algorithm that can be used to solve both classification and regression problems. It works by (i) finding the distances between a query and all the examples in the data, (ii) selecting the K nearest neighbors of the query, and (iii) voting for the most frequent label (in the case of classification) or averaging the labels (in the case of regression).
	Support vector machines (SVMs) [38]	SVMs is a supervised method that identifies a hyperplane that best divides the data into two classes. To separate the two clouds of data points, there are many possible hyperplanes that could be chosen. The objective of the SVM algorithm is to find a slab that has the maximum thickness, i.e., the maximum distance between data points of the different classes.
	Shallow artificial neural networks (ANNs) [39]	The ANN vaguely simulates the way the human brain analyzes and processes information. They consist of sequential layers: input, hidden and output layers. The hidden layer processes and transmits the input information to the output layer.
Deep Learning	Convolutional neural networks (CNNs) [40]	This is a class of DL algorithms commonly used in computer vision and pattern recognition. CNNs are a specific type of neural networks that are generally composed of the following layers: (i) input layer, (ii) convolution layers, (iii) pooling layers and (iv) fully connected layers. The convolution layers use filters that perform convolution operations as they are scanning the input with respect to its dimensions. Pooling is a down-sampling operation, which is typically applied after a convolution layer. The fully connected layers operate on a flattened input where each input is connected to all neurons in the next layer and are usually found towards the end of CNN architectures to optimize objectives such as class scores.
	Region based convolutional neural networks (R-CNNs) [41]	The method of detecting and classifying objects in an image is known as object detection. R-CNN (regions with convolutional neural networks) is a deep learning technique that blends rectangular area proposals with convolutional neural network functionality. The R-CNN algorithm is a two-stage detection method.
	Deep residual networks [42]	A residual neural network (ResNet) is an ANN variant that uses residual mapping and shortcut connections to tackle the problem of vanishing and exploding gradients that is characteristic of deep CNNs. As a consequence of this, deep residual networks achieve better performance when compared to plain very deep networks, whereas their training is easier as well. Typical ResNet models are implemented with double- or triple-layer skips that contain nonlinearities such as rectified linear unit (ReLUs) and batch normalization in between.
	3D-CNNs [43]	A 3D CNN is simply the 3D generalization of 2D CNNs. It takes as input a 3D volume or a sequence of 2D frames (e.g., slices in an MRI scan). Then kernels move through 3 dimensions of data producing 3D activation maps. Overall, they learn powerful representations of volumetric data.
	Computer Vision Transformers [44]	When data is modelized as a sequence of embeddings, the Transformer model is a basic yet scalable technique that can be used for any type of data. Even without typical convolutional pipelines, transformers can be utilized to provide SOTA results in Computer Vision. It is a DL network that extracts inherent properties of the interest domain via the self-attention technique.
Procedure	Training	The standard procedure involves a dataset of paired images and labels (x, y) for training and testing, an optimizer (e.g., stochastic gradient descent, Adam [45]), and a loss function to update the model parameters. The aim of the training is to find the optimal values for the network parameters so that the loss function is minimized.
	Data augmentation	Data augmentation is a strategy that artificially generates more training samples to increase the diversity of the training data. This can be done via applying affine transformations (e.g., rotation, scaling), flipping or cropping to original labeled samples.
	Dropout	Dropout is a regularization method that randomly drops some units from the neural network during training, encouraging the network to learn a sparse representation. It is used to reduce overfitting.
	Loss function	The metric to assess the discrepancy between model predictions and labels is called loss function. The gradients of the loss function are used to update the weights of the neural networks.
	Transfer learning	This aims to transfer knowledge from one task to another different but related target task. This is often achieved by reusing the weights of a pre-trained model, to initialize the weights in a new model for the target task. Transfer learning can help to decrease the training time and achieve lower generalization error.