This paper proposes a U-Net-based deep learning architecture for the task of super-resolution lower resolution brain magnetic resonance images (MRI). The proposed system, called MRI-Net, is designed to learn mapping between low-resolution and high-resolution MRI images. system trained using 50-800 2D scans, depending on architecture, evaluated peak signal-to-noise ratio (PSNR) metrics 10 random...

This paper intends to offer a new iterative method based on articial neural networks for finding solution of a fuzzy equations system. Our proposed fuzzied neural network is a ve-layer feedback neural network that corresponding connection weights to output layer are fuzzy numbers. This architecture of articial neural networks, can get a real input vector and calculates its corresponding fuzzy o...

Segmentation of images is a common task within medical image analysis and necessary component segmentation. The segmentation the liver tumors an important but challenging stage in screening diagnosing diseases. Although many automated techniques have been developed for tumor segmentation; however, still due to fuzzy & complex background position with other organs. As result, creating consid...

Semantic Segmentation using deep convolutional neural network pose more complex challenge for any GPU intensive work, as it has to compute million of parameters resulting to huge consumption of memory. Moreover, extracting finer features and conducting supervised training tends to increase the complexity furthermore. With the introduction of Fully Convolutional Neural Network, which uses finer ...

2 Neural Nets, Statistical Physics and Optimization It has recently been shown that neural net models can be based upon the principles of statistical physics, In particular the spin sociative memories and optimization networks. The simulation of such neural networks promises to provide valuable insights into human and machine vision. However, for a large amay, the calculations involved become p...

Previous work on nets with continuous-valued inputs led to generative procedures to construct convex decision regions with two-layer perceptrons (one hidden layer) and arbitrary decision regions with three-layer perceptrons (two hidden layers). Here we demonstrate that two-layer perceptron classifiers trained with back propagation can form both convex and disjoint decision regions. Such classif...

In recent years, as machine learning has been widely studied in the field of architecture, scholars have demonstrated that computers can be used to learn graphical features building façade generation. However, existing deep generation yet generate only a single façade, without comprehensive five façades including roof. Moreover, most literature utilized Pix2Pix algorithm for experiments, failin...