of convergence is improved by using nonlinear inversion techniques [10; 11]. However, it is computationally complex and therefore cannot be used for real-time applications. On the other hand, radar-based imaging aims to localise strong reflected scatters in the area of interest. There are two types of radar-based beamformers: data-independent [12] and data-adaptive beamformers [13]. In data-independent beamforming algorithms, the coherent addition of signals is performed on the basis of an assumed propagation model. Data-adaptive algorithms, on the other hand, use scattered signals to estimate the propagation model and to then apply compensation factors based on the estimated channel model. Data-independent beamformers take less time in image reconstruction, while data-adaptive beamformers have excessive computational complexity but produce accurate results [14]. Radar-based beamforming techniques in microwave imaging aim to reconstruct a reflective map of the imaged tissues, where high contrast regions (malignant tissue) are prominently detected. The basic image reconstruction method in radar-based microwave imaging is the delay and sum (DAS) beamformer [15]. This technique thoroughly delays the collected time domain signals and sums it up to reconstruct an image. Several improvements have been made to the DAS beamformer, including delay multiply and sum (DMAS) [16], improved delay and sum (IDAS) [15], and channel-ranked delay and sum (CR-DAS) [17]. These improvements to the DAS have primarily focused on coherence between reflected or transmitted radar signals or on tackling path dependency. A recent study compared these radar-based algorithms using clinical data for breast cancer detection. The results demonstrated that DMAS produces more accurate and high-resolution results [18]. It reduces clutter and background noise that leads to more accurate localisation of the tumour in resulting images [19]. The computational complexity of DAS is O(M). The DMAS gains better resolution and contrast at the cost of increasing the computational complexity to O(M\({}^{2}\)) [16]. The advantages of using DMAS over DAS has been proven in [20; 21]. The iterative variant of DMAS was proposed in [22], where the background clutter was removed in the resulting images. Due to its advantages, DMAS is used in many research works instead of DAS [22]. The downside of using DMAS in comparison to DAS is the significant increase in computational load [23]. Hadoop and MapReduce are commonly used in commerce, social media and video streaming. However, they are not widely integrated into the healthcare sector. For example, a pipeline for functional magnetic resonance imaging (fMRI) was proposed using the PySpark [24]. In the proposed pipeline, the template method is used to extricate brain networks from fMRI data. The pipeline is four times faster due to in-memory data processing in parallel compared to sequential execution. Similarly, Spark was used for genomics sequencing to reduce the processing time [25]. Spark cloud computing platform was used for parallel implementation of the MAX-MIN Ant System algorithm [26]. Experimental results showed significant improvement in speed for a large number of ants. Similarly, the most commonly used K-means clustering algorithm was improved in terms of scalability, accuracy and effectiveness by using a Tabu Search Strategy and Spark framework [27]. Computational experiments indicated that the proposed parallel algorithm yields better results in terms of quality, stability and efficiency than the K-means algorithm of SparkMLLib. Field programmable gates arrays [28] and graphics processing unit-based parallel approaches are mostly used to decrease computation time [29]. A GPU-based implementation of CMI algorithms was presented for breast cancer detection [30]. Although these approaches significantly increase processing speed, they are non-scalable and costly.