Medical image segmentation plays a key role in identifying the disease type. In the last decade, various methods have been proposed for medical images segmentation. Despite many efforts made in medical imaging, segmentation of medical images still faces challenges, concerning the variety of shape, location, and texture quality. According to recent studies and magnetic resonance imaging, segmentation of brain images at around 6 months of age is a challenging issue in brain image segmentation due to low tissue contrast between white matter (WM) and grey matter (GM) regions. In this study, using the deep learning model, the convolutional network for the brain fragmentation is presented. First, the image quality is improved using the pre‐processing method. The number of layers utilised in the proposed method is less than that of known models. In the pooling layer, instead of using the maximum function, the averaging function is employed. Sixty‐four batches are also considered to improve the performance of the proposed method. The method is evaluated on the iSeg‐2017 database. The DISC and ASC measures of the proposed method for the three classes of GM, WM, and cerebrovascular fluid are 0.902, 0.594, 0.930, 0.481, 0.971, and 0.231, respectively.
The electricity consumption forecast is especially important with regard to policy making in developing countries. In this paper, the electricity consumption rate is predicted using the data mining techniques. The datasets that were collected for predicting the electricity consumption are related to Islamic Republic of Iran -Mazandaran province pertaining to the years 1991 to 2013. The research objective is analyzing the electricity consumption rate in recent years and predicting future consumption. According to a study the electricity consumption growth rate between the years 2006 to 2013 and the years 1999 to 2006 equaled 28.41 and 73.53, respectively. The results of the research conducted using the regression model indicate a 2.48 relative error. The output of this prediction shows that the total electricity consumption rate increases about 3.2% annually on average and will reach 7076796 megawatts by the year 2020 that shows a 22.28% growth comparing to the year 2013.
Image segmentation is an essential step in image processing. Many image segmentation methods are available but most of these methods are not suitable for noisy images or they require priori knowledge, such as knowledge on the type of noise. In order to overcome these obstacles, a new image segmentation algorithm is proposed by using a self-organizing map (SOM) with some changes in its structure and training data. In this paper, we choose a pixel with its spatial neighbors and two statistical features, mean and median, computed based on a block of pixels as training data for each pixel. This approach helps SOM network recognize a model of noise, and consequently, segment noisy image as well by using spatial information and two statistical features. Moreover, a two cycle thresholding process is used at the end of learning phase to combine or remove extra segments. This way helps the proposed network to recognize the correct number of clusters/segments automatically. A performance evaluation of the proposed algorithm is carried out on different kinds of image, including medical data imagery and natural scene. The experimental results show that the proposed algorithm has advantages in accuracy and robustness against noise in comparison with the well-known unsupervised algorithms.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.