## 1 Introduction  
Future wireless medical technologies are expected to explosively grow for controlling the bodily functions and to gather the data of different physiological parameters [1]. Over the last decade, implanted devices, especially implanted antennas, have got the attention of researchers due the compatible connection between the patient and physical doctors [2, 3]. Multiple-input, multiple-output (MIMO) antennas are commonly used in the wireless communication systems due to the advanced capability in supporting data even under interference situations [4, 5]. Hence, designing MIMO antennas that can be used for medical communication services can be a very good solution to increase the accuracy of the system.  
Due to the complexity of MIMO antennas, it is not straightforward to model and design these circuits, and optimization-based approaches are importantly required. Reported various optimization methods around radio frequency and antenna designs are particle swarm optimization [6, 7], ant colony optimization [8, 9], chicken swarm optimization [10], harmony search algorithm [11], and genetic algorithm [12]; however, when the design parameters are in a huge number these methods can not be successful and intelligent based optimization methods are required [13].  
Recently, machine learning and deep learning proved their talents in optimizing radio frequency designs and they can be a good solution for modeling and optimizing MIMO antennas [14]. They can model the complex designs by considering the relationships between the input and output data that in turn can be design parameters and/or design specifications, respectively [15].  
This paper presents an automatic methodology for designing and optimizing implanted MIMO antennas; to the best of authors' knowledge, the proposed method is for the very first time reported in the literature. This optimization-oriented process is performed by using two sequential methods as hierarchy top-down optimization (TDO) and regression _deep neural network (DNN)_ for configuring the antenna geometry and sizing the design parameters, respectively. The TDO method is employed for constructing the general structure of the antenna and the DNN, a multi-layer neural network, with long short-term memory (LSTM) layers is used for predicting the optimal design parameters more accurately than the shallow neural networks (SNNs) [16]. As constructing the accurate DNN is significant, _bayesian optimization (BO)_ is applied for determining the optimal hyperparameters. All the optimization process is executed in the environment where Microwave Studio (Dassault Systemes) and numerical analyzer as MATLAB are working concurrently. The proposed method leads to generate electromagnetic (EM)-verified post layout generation and dependency on designers' experiences is substantially reduced. Moreover, the difficulty of modeling and sizing complex design of implanted MIMO antenna is solved to the most highest degree.  
This paper is organized as follows: Section 2 in a nutshell, presents the proposed methodology for modeling and sizing the implanted MIMO antennas. Section 3 is devoted to explain the practical construction of the proposed method, while Section 4 describes the simulation results of optimized antenna. Finally, Section 5 concludes this work.