# MR Images, Brain Lesions, and Deep Learning
## 2 The Literature Review: Selection Criteria
### Computer-Aided Diagnosis in Medical Imaging (CADx System)
#### 3.2.4 Feature Extraction  
An ML or DL algorithm is often a classifier [148] of objects (e.g., lesions in medical images). Feature selection is a fundamental step in the processing of medical images, and especially, it allows us to research which features are relevant for the specific classification problem of interest, and also it helps to get higher accuracy rates [47].  
The task of feature extraction is complex due to the task of determining an algorithm that can extract a distinctive and complete feature representation, and for that principal reason, it is very difficult to generalize and implies that one has to design a featurization method for every new application [115]. In DL, this process is also called hand-crafting features [115].  
The classification is related to the extracted features that are entered as input to an ML model [148], while a DL algorithm model uses pixel values in images directly as input information instead of features calculated from segmented objects [148].  
In the case of processing stroke with CNNs, the featurization of the images is a key application [75; 124] and depends on the signal-to-noise ratio in the image, which can be improved by target identification via segmentation to select regions of interest [124]. According to [PERSON] et al., [193], a CNN learns to discriminate local features and returns better performance than hand-crafted features.  
Texture analysis is a common technique in medical pattern recognition tasks to determine the features, and for that, one uses second-order statistics or co-occurrence matrix features [45]. [PERSON] et al. [182] indicate that they derive local features, spatial features, and context-rich features from the input MRI channels.  
It is clear that currently, DL algorithms, especially those that use a combination of CNNs and machine learning classifiers, produce a marked transformation [197] in the featurization and segmentation in medical image processing [16; 124]. CNNs have high utility in tasks like identification of compositional hierarchy features and low-level features (e.g., edges), specific pattern forms, and development of intrinsic structures (e.g., shapes, textures) [5], as well as spatial feature generation from an \(n\)-dimensional array of basically any arbitrary size [43; 144], e.g., the U-Net model proposed by [PERSON] et al. [141], which employs parameter sharing between encoder-decoder paths for incorporating spatial and semantic data that allow better segmentation performance [179]. Based on the U-Net model, currently there are novel variants of U-Net designs. For example, [PERSON] et al., [198] used a U-net architecture with 3D convolutions that allow the use of an attention gate for the decoder to suppress unimported parts of the input, while emphasizing the relevant features. There is considerable room for improvement and innovation (e.g., [199]).  
The process of converting a raw signal into a predictor (automatization of the featurization) constitutes an advantage of the DL methods over others, which is useful when there are large volumes of data of uncertain relationship to an outcome [124], e.g., the featurization of acute stroke and demyelinating diseases.