## 3 Methodology
### Feature Extraction
#### 3.3.1 Hand-Crafted Features  
* Statistical textural features: Statistical textural features result from the quantitative analysis of the pixel intensities in the grayscale image using different arrangements. Intensity histograms, first-order statistical textures, gray-level co-occurrence matrices (GLCM) and gray-level run-length matrices (GLRLM) are used as the feature descriptors to extract the statistical textural features. We extracted eight first-order statistical features [33], a total of 88 GLCM features, which encoded 22 different features in four directions [34], and a total of 44 GLRLM features which encoded 11 different features in four directions [35], for a total of 140 textural features. * Local binary pattern (LBP) features: An LBP is a texture histogram that describes a texture based on differences between central pixels and its neighbors. LBP produces a binary pattern using a threshold value for the central pixel with its neighborhood. A neighbor is 1, when it is greater than or equal to the central pixel, and 0 when it is less. Then the frequency of binary patterns is determined as a histogram of the representative number of binary patterns found in the image [36]. With an 8-pixel neighborhood, 256 features are obtained. * GIST features: GIST is a feature descriptor that proceeds image filtering to develop a low-level feature set including intensity, color, motion, and orientation based on the information of the gradients, orientations, and scales of the image [37]. GIST captures these features toward identifying the salient image locations that significantly differ from those of the neighbors [14]. First, GIST convolves a given input image with 32 Gabor filters at four different scales and eight different orientations to generate a total of 32 feature maps. Each of these feature maps was then splatted into 16 sub-regions with a \(4\times 4\) square grid and the feature values within each  
Figure 5: Data flow for lung segmentation using DeepLabv3+. sub-region were averaged. The averaged values from the 16 sub-regions were concatenated for the 32 different feature maps, resulting in a total of 512 GIST descriptors for a given image. * Histogram of oriented gradients (HOG) Features: A HOG descriptor, introduced by [PERSON] and [PERSON] [38], counts gradient orientation occurrences in localized image regions. HOG measures the first-order image gradient pooled in overlapping orientation bins, and gives a compressed and encoded version of an image. It first computes gradients, creating cell histograms, and generating and normalizing the descriptor blocks. Given an image, HOG first fragments the image into to small-connected regions called cells. Following this, it computes the gradient orientations over each cell and plots a histogram of these orientations, giving the probability for a gradient with a specific orientation in a given path. The adjacent connected cells are grouped into small blocks. The features are extracted over small blocks, in a repetitive fashion, to preserve information about local structures, and the block-wise features are finally integrated into a feature vector. We used the cell size of [\(16\times 16\)] pixels, number of bins 3, and \(4\times 4\) cells in each block. A total of 10,800 HOG features were extracted. * Pyramid histogram of oriented gradients (PHOG) features: [PERSON] et al. 's PHOG descriptor [39], represents an image by its spatial layout and local shape. First, PHOG tiles the image into sub-regions, at multiple pyramid-style resolutions, and in each sub-region, the histogram of orientation gradients is applied as a local shape descriptor using the distribution of edge directions. We extracted a total of 168 PHOG features from each image. * Bag of visual words features: BoVW is a technique adapted from information theory to computer vision applications [40]. Contrary to text, images do not contain words, so, this method creates a bag of features extracted from the images across the classes, using a custom feature descriptor, and constructs a visual vocabulary.