Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset.
: Recently, the technology has developed and the wireless sensor networks (WSNs) have received great interest and have attracted the attention of the entire world. Wireless sensor network is a large set of sensor nodes that work to move or follow different phenomena. These sensor nodes are specific resources (energy, memory, etc.). Once the power is completed or equipped with the sensor node, this causes the sensor node to lose permanently because the sensor node cannot be processed again if the power supply is finished. It is necessary to pay attention to it well as it is the biggest challenge in wireless sensor networks. Military and border surveillance WSNs are becoming an integral part of military command, control, communication and intelligence systems. So it is preferred to encrypt the data transmitted through these networks. In WSNs, due to relatively smaller size and low computation power, elliptic curve cryptography is very popular public key encryption technique. However, such encryption operation in turn consumes further energy. This paper suggests two approaches to enhance the energy consumption of the structure free WSNs with encrypted data. The first is based on spread spectrum technology, while the other approach deals with the mechanism of selecting the sensing and the next node of the routing path. The results show an improvement in energy consumption of 40% and 12% when the first and the second approaches are used respectively.
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