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Abstract-Molecular communication (MC) has recently emerged as a novel paradigm for nano-scale communication utilizing molecules as information carriers. In diffusion-based molecular communication, the system performance is constrained by the inter-symbol-interference (ISI) caused by crossover of information carrying molecules in consecutive bits. To cope with this, we propose the Reed-Solomon (RS) codes as an error recovery tool, to improve the transmission reliability in diffusionbased MC systems. To quantify the performance improvement due to RS codes, we derive the analytical expression for the approximate bit error probability (BEP) of the diffusion-based MC system with the full absorption receiver. We further develop the particle-based simulation framework to simulate the proposed system with RS code to verify the accuracy of our derived analytical results. Our results show that, as the number of molecules per bit increases, the BEP of the system with RS codes exhibits a substantial improvement than that of non-coded systems. Furthermore, the BEP of the proposed system with RS codes can be greatly improved by increasing the minimum distance of the codeword.

One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a threefold deep learning architecture. First, classifiers are implemented with a deep convolutional neural network (CNN) and second a region-based convolutional neural network (R-CNN) is performed on the classified images to localize the tumor regions of interest. As the third and final stage, the concentrated tumor boundary is contoured for the segmentation process by using the Chan–Vese segmentation algorithm. As the typical edge detection algorithms based on gradients of pixel intensity tend to fail in the medical image segmentation process, an active contour algorithm defined with the level set function is proposed. Specifically, the Chan–Vese algorithm was applied to detect the tumor boundaries for the segmentation process. To evaluate the performance of the overall system, Dice Score, Rand Index (RI), Variation of Information (VOI), Global Consistency Error (GCE), Boundary Displacement Error (BDE), Mean Absolute Error (MAE), and Peak Signal to Noise Ratio (PSNR) were calculated by comparing the segmented boundary area which is the final output of the proposed, against the demarcations of the subject specialists which is the gold standard. Overall performance of the proposed architecture for both glioma and meningioma segmentation is with an average Dice Score of 0.92 (also, with RI of 0.9936, VOI of 0.0301, GCE of 0.004, BDE of 2.099, PSNR of 77.076, and MAE of 52.946), pointing to the high reliability of the proposed architecture.

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