Cancer is one of the leading causes of death, and the brain is one of the body's cancerprone organs. The early detection of brain tumors can reduce cancer risk, which is practically assisted and conducted using scanners such as computed tomography (CT) and magnetic resonance imaging (MRI). However, those modalities are high-cost and large-sized, and they have a side effect risk to health. Alternatively, microwave imaging offers a novel cancer scanning method for early detection with low cost, small size and low health risk. Consequently, this research designs and creates a framework with a novel microwave image reconstruction algorithm inside. The framework is a component of the controller and image reconstructor for a portable microwavebased brain tumor detector that is open source and multi-platform. For the novel algorithm, this research proposes a CS-based imaging algorithm by exploiting the data's sparse and low-rank properties. The experiment shows that the proposed algorithm can give better qualitative and quantitative reconstruction results compared to a full-sampling-based as well as CS-based algorithm.
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