# Magic of 5G Technology and Optimization Methods Applied to Biomedical Devices: A Survey
## 4 Optimizations for Healthcare and Biomedical Devices
### Optimizations Using the Artificial Neural Network (ANN)  
Recently, artificial intelligence, big data analytic, and machine learning have become successful optimization tools and become subjects of industrial interests [72; 73; 74]. [PERSON] et al. presented the use of an artificial neural network (ANN) for estimating the magnetic resonance imaging (MRI) radio frequency (RF) exposure for the implantable plate system where it considers the existing problems using the non-linear and high-dimensional features [75]. The general performance of the ANN is improved by using the mean impact value (MIV) and genetic algorithm (GA). The presented ANN in [75] includes six inputs, two hidden layers, and one output layer with the transfer function of 'tan-sigmoid' where the last outcomes are considered using the mean square error (MSE) algorithm. In this manuscript, the MIV algorithm is employed for deciding the inputs of the ANN and feature selections; additionally, the GA method is for optimizing and adjusting the weights and bias of the ANN. In [76], deep neural network (DNN) architecture is presented to be used in the biomedical image segmentation. The proposed method is named the evolutionary compression DNN method (ECDNN), where during the evolution, this method can simultaneously optimize the network loss and the number of parameters. In this study, the framework of the proposed ECDNN that is useful for biomedical image segmentation is presented. It consists of three steps where firstly the convolutional neural network (CNN) is constructed using the training dataset. Then, ECDNN reduces the size based on the CNNs and the searching of Pareto-optimal optimization. Thirdly, the final solution is selected from the Pareto-optimal set to compress the original model and update the method if required. The presented procedure is fast as it finds a solution for compressing CNNs in a single run. In clinical uses, the accuracy and robustness factors of brain-machine interfaces (BMIs) are key important specifications as BMIs could provide a situation for driving the robotic arm and computer cursor. [PERSON] and [PERSON] proposed the DNN method for optimizing encoders and synthesizing the motor cortical neural population activity [77] (see Figure 19). Figure 18: Geometrical coil optimization used for brain neural implant [71]. In [78], the neural network (NN) is employed for use in electrocardiogram (ECG) processor. For accurate authentication, the front-end signal processing of ECG signals and back-end NNs is presented. The NN is used for minimizing and maximizing the intra-individual distance and inter-individual distance over time, respectively. Figure 20 presents the schematic of the ECG processor that includes the front-end signal processing modules and back-end NN-based ECG feature extraction modules. The proposed system achieves 62.37 uW power consumption at 1.2 V supply voltage with a low equal error rate (EER) of 1.36%. Image segmentation and classification is another promising computation in diagnosing diseases. [PERSON] et al. presented the classification DNN for diagnosing lung cancer, brain image, and Alzheimer's diseases [79]. The opposition-based crow search (OCS) algorithm was employed to enhance the performance of the DNN, leading an optimal feature selection being derived. Figure 21 illustrates the flowchart of the presented classification DNN  
Figure 19: (**a**) BMI system and neural encoder (**top**); (**b**) multilayer perceptron (MLP) architecture (**bottom**). Figure 20: Employed NN for ECG processor. resulting in an accuracy of 95.22%, a sensitivity of 86.45%, and a specificity of 100% for the used medical images. [PERSON] presented the IoT-centered deep learning modified neural network (DLMNN) that is performed in three steps as follows: authentication, encryption, and classification [80]. The employed classification DNN determines whether the patient is suffering from heart disease and in the case of an abnormal situation, the system alerts the physician.