## 1 Introduction
It is non-ionizing with emission power less than cellular devices radiation and less system complexity similar to ultrasound technology. Furthermore, it offers safe, low-cost, and portable method for medical imaging applications [12]. The downside of microwave imaging is it does not create images of high resolutions as compared to CT and MRI. However, due to its advantages, many research groups are exploiting improved antenna design, imaging setup optimization, tissue scattering modeling, development and optimization of image reconstruction algorithms, and in vitro and in vivo validation of these imaging systems. They have successfully designed and developed prototypes, as well as conducted trials on volunteers and/or patients, mainly targeting breast [13; 14] and brain imaging [15; 16; 17]. Large-scale image processing applications, especially those involving multistage analysis, often indicate substantial inconsistency in processing times ranging from a few seconds to several hours [18]. Microwave techniques in medical imaging and remote sensing applications reconstruct the image of dielectric regions to retrieve the location, shape, and size of the targeted object. For many years, new optimization algorithms have been studied to address the compute-intensive inverse scattering problem associated with these techniques [19]. Field programmable gates arrays (FPGAs) and graphics processing unit (GPU)-based parallel approaches are commonly used to solve problems of image processing, especially in multi-modality image diagnoses, that require huge processing time [20; 21]. Although, these approaches improve processing speed significantly, they are limited by non-scalability and higher cost. The use of parallel or cloud computing technology for image-guided diagnostics is a recent trend and has caught the attention of many researchers in the field [22]. Some of the potential reasons to use cloud computing for medical image analysis are huge data storage, remote availability, faster processing demands, and scalability. In order to enhance the efficacy and accuracy of imaging modalities, medical imaging societies need to consider and embrace parallel approaches using high-performance computing (HPC) or cloud computing technologies as accelerators and use them as powerful tools for multi-modality imaging data analysis. Big data frameworks, such as Hadoop and Spark, are getting attention as it makes possible the scalable utilization of these parallel systems [23]. However, parallel implementation of algorithms on these frameworks faces several technical challenges depending on the level of their complexity. An inexpensive solution is to place the required data on the computational nodes to avoid network saturation. Hadoop and Map-reduce provide tools to address this issue; however, they are still not widely being integrated into medical image processing, despite having shown great success in online commerce, social media, and video streaming [24]. Data acquisition from sensors and its image reconstruction process using different algorithms and machine learning (ML) techniques is a recent trend and is gaining a lot of interest [25]. Consequently, to make it more efficient and scalable, healthcare entities are adopting big data frameworks, for instance, Apache Hadoop and Spark [26]. These are the most common big data processing frameworks being used for the implementation of the algorithm over distributed systems. The main goal is to process a huge amount of data on a cluster of hundreds or thousands of machines in parallel, which helps in the real-time decision-making process. This paper aimed to contribute to this growing area of research by exploring the use of Apache Spark on HPC and Google Cloud Platform (GCP) for microwave imaging of head diagnostics. This work has made use of Eddie [27] provided by Edinburgh Compute and Data Facility. Eddie is the third generation HPC cluster running a Linux operating system. It consists of some 7000 Intel(r) Xeon(r) cores, with up to 3 TB of memory available per compute node.