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Cerebral microbleed (CMB) the small vessels in the brain which is one of the major factors used to facilitate in the early stage diagnosis for Alzheimer’s disease detection. In traditional, CMBs detection can be done manually by the neurologists, doctors or specialists. However, the process is time-consuming and the results are not accurate depending on the doctor experiences. Therefore the efficient and reliable of the automatic detection of CMB is needed. This paper proposes a new framework for CMB detection which employs segmentation of the region of interests (ROIs), detection of the CMBs and identification of the area from SWI scan images. Convolutional Neural Network(CNN) is applied to generate the desired models for later prediction. Shape matching mechanism is also applied to identify locations of CMB in the brain. The experimental result shows that the CMB can be classified with a recorded accuracy value of 95.45%. The CMBs were discovered from three different locations include (i) cortical region, (ii) cerebellum and (iii) brainstem with an accuracy value of 100%.

Abstract. Many countries around the world regularly collect census data. This census data provides statistical information regarding populations to in turn support decision making processes. However, traditional approaches to the collation of censes data are both expensive and time consuming. The analysis of high resolution satellite imagery provides a useful alternative to collecting census data which is significantly cheaper than traditional methods, although less accurate. This paper describes a technique for mining satellite imagery, to extract census information, founded on the use of classification techniques coupled with a graph based representation of the relevant imagery. The fundamental idea is to build a classifier that can label households according to "family size" which can then be used to collect census data. To act as a focus for the work training data obtained from villages lying some 300km to the northwest of Addis Ababa in Ethiopia was used. The nature of each household in the segmented training data was captured using a tree-based representation. Each tree represented household had a "family size" class label associated with it. This data was then used to build a classifier that can be used to predict household sizes according to the nature of the tree-based structure.