## 2 Preliminaries
### Deep Learning (DL)  
DL is a type of AI that uses networks of algorithms called deep neural networks to learn and make decisions from large amounts of data. It works by recognizing patterns in data, such as images or text [53], and improving its accuracy over time. Deep learning is used in many applications, like speech recognition [54], image analysis [55], and healthcare predictions [56]. DL generally has five different approaches, as shown in Table 2. It usually requires five stages: data preparation, model training, model optimization, model evaluation, and model deployment. DL can be constructed with different models to deal with different problems. Common DL models include the following:  
* Convolutional neural networks (CNNs): A CNN is a specialized deep learning model tailored for analyzing image and visual data. It is widely utilized in medical imaging for tasks such as image classification, segmentation, and anomaly detection, including tumor identification in MRI and CT scans as well as fracture detection in X-rays. Its capability to automatically extract spatial features from images makes it a valuable tool in diagnostic imaging [73]. Unlike traditional rule-based or handcrafted feature extraction methods, which rely on manually defined edge detection filters or statistical techniques, CNNs learn hierarchical representations from raw data, significantly improving accuracy and robustness. Traditional feature extraction approaches, such as edge detection filters (Sobel, Canny) [74] or handcrafted descriptors (HOG, SIFT, LBP) [75], require domain expertise to define relevant features. These methods often struggle with variations in lighting, contrast, and texture, leading to inconsistent results in medical imaging. In contrast, CNNs apply trainable convolutional filters to recognize patterns such as edges, textures, and anatomical structures directly from images, without manual intervention [76]. CNNs employ various convolution techniques [77], such as local connections, weight sharing, and pooling layers, to reduce data dimensions while preserving crucial information. This automation allows CNNs to generalize better across diverse datasets, making them more effective for large-scale, real-world medical applications [78]. \begin{table}
\begin{tabular}{l l} \hline \hline
**Deep Learning Approach** & **Subcategories/Description** \\ \hline Supervised Learning [57] & \(\bullet\) & Classification: Predict discrete labels (e.g., image recognition [58], spam detection [59]). \\  & \(\bullet\) & Regression: Predict continuous values (e.g., predicting house prices [60], stock forecasting [61]). \\ \hline Unsupervised Learning [62] & \(\bullet\) & Clustering: Group similar data points (e.g., customer segmentation [63], pattern recognition [64]). \\  & \(\bullet\) & Dimensionality reduction: Reduce data complexity (e.g., PCA [65], t-SNE [66]). \\ \hline Semi-Supervised Learning [67] & \(\bullet\) & Utilizes a small amount of labeled data with a large amount of unlabeled data to enhance learning performance, such as in text classification. \\ \hline Reinforcement Learning [68] & \(\bullet\) & Q-learning [69]: A value-based method to find optimal actions. \\  & \(\bullet\) & Policy gradient [70]: Learning policies directly to maximize rewards. \\ \hline Federated Learning [71] & \(\bullet\) & A decentralized approach involves training models locally on different devices and then combining them (e.g., distributed learning in mobile networks and privacy-preserving models [72]). \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Different approaches and architectures in DL. * Recurrent neural networks (RNNs): An RNN is a specialized artificial neural network for recognizing patterns in sequential data. Examples of such data include time series, speech, or text. Different from traditional feedforward neural networks that process inputs in one direction, RNNs have connections forming directed cycles. This makes them retain a form of'memory' of previous inputs through their internal state.