## 1 Introduction  
Due to the availability of new data sources, there is a need to rethink existing architectures that enable big data analytics, especially in healthcare. Some sources include mobile phones (sensors), genomics, sensor networks, social media, internet of things, and medical imaging modalities that determine medical diagnostic and medical records. All these sources produce data at a rapid pace, which needs to be stored and processed somewhere in an efficient way [1]. Storing and analyzing such a huge amount of data is a challenge, especially when it does not conform to the traditional notion of data structures. Big data characteristics include huge volume, wide variety, high velocity, veracity, and value. The processing of these data requires new architecture, new tools, and new analytical methods [2]. Distributed and parallel computing architectures are preferred approaches to process such large scale unstructured and semi-structured data. Using technologies, such as Hadoop and Spark, to perform natural language processing on these data can significantly improve performance and create valuable information. The image data processing is an important aspect of clinical diagnosis; for example, the correct analysis and interpretation of images are vital for the early detection of diseases [3]. The use of state-of-the-art devices for medical imaging, such as X-ray, magnetic resonance imaging (MRI), computerized tomography (CT), and positron emission tomography (PET), has greatly improved data acquisition process. These imaging modalities are being used for spatial representation of different biological tissues for different diagnosis. CT combines multiple x-ray scans to reconstruct the image representing a slice/volume of the imaged object. The advantages of CT imaging include its excellent resolution and contrast, along with being relatively faster than MRI. However, it requires larger dose of ionizing radiation, as well as being slower and more complex, compared to planar X-ray. X-ray and CT expose patients to ionizing radiation that increases the risk of cell mutation. Moreover, the equipment is costly and therefore not available at all hospitals [4]. Conversely, ultrasound is a low-cost solution, due to its cost-effectiveness and portable ability. The disadvantages of ultrasound is that it requires a good physical contact with the imaged tissue for a high wave impedance contrast to reconstruct high resolution image [5]. Photoacoustic imaging (PAI) is an emerging modality for imaging human tissues based on thermoelastic effect. This imaging modality used short laser pulse to reconstruct the optical absorption map of the human tissue. The main advantages of PAI is combining the high spatial resolution of ultrasound and high contrast of optical imaging. PAI is a multiscale imaging modality used in different preclinical and clinical applications, such as tumor detection [6] and functional imaging [7]. The downside of PAI are inherent artifacts in the reconstructed images caused by imperfect reconstruction algorithms [8]. The most common and one of the newest imaging modality is MRI. It is a tomographic technique based on the phenomenon of nuclear magnetic resonance. It produces high-quality images having excellent spatial resolution in the order of millimeters and is more suited for imaging soft tissues (e.g., distinguishing white and grey matter in the human brain) due to higher concentration of the hydrogen molecules. The disadvantages of MRI are its high cost of equipment and maintenance, system complexity and time needed for data acquisition [9]. A single scan of MRI takes about 5 min. Furthermore, MRI is not suitable in different situations, such as for patients with a heart pacemaker, as magnetic fields can upset the operations of pacemakers. In addition, heating can occur in some metallic implants [10]. Microwave imaging is an alternative imaging modality being investigated for different biomedical applications, such as breast cancer and brain imaging [11].