Implementation of a Modified U-Net for Medical Image Segmentation on Edge Devices

Deep learning techniques, particularly convolutional neural networks, have shown great potential in computer vision and medical imaging applications. However, deep models are computationally demanding as they require enormous computational power specialized processing hardware for model training. To make these portable compatible prototyping, their implementation on low-power devices is imperative. In this work, we present the of Modified U-Net Intel Movidius Neural Compute Stick 2 (NCS-2) segmentation images. We selected because, image segmentation, a prominent that provides improved performance even if dataset size small. The modified evaluated terms dice score. Experiments reported task three datasets: BraTs brain MRI, heart MRI dataset, Ziehl-Neelsen sputum smear microscopy (ZNSDB) dataset. For proposed model, reduced number parameters from 30 million to 0.49 architecture. Experimental results show comparable while requiring significantly lower resources inference NCS-2. maximum scores recorded 0.96 0.94 0.74 ZNSDB