Uploaded on Apr 14, 2020
Cerebral Microbleeds are the chronic Brain haemorrhages. They have been recognized as the important parameter for many cerebrovascular diseases. In the current situation, the cerebral Microbleeds are manually labelled by radiologists but this procedure is so difficult, time consuming and error may occur. In this paper we propose a Maxpooling method and Convolutional neural networks to detect the cerebral Microbleeds. Compared with the previous methods the detection is based upon the feature extraction process and they used the sliding window approach, and the detection of cerebral Microbleeds is only done. Our method is to segment the cerebral images by the Maxpooling, Rectifier Linear unit (ReLU), and fully connected layers. To extract the image features, HOG and the statistical features are used. Log Softmax function is used to predict the final Image. Disease classification is also done support vector machine. First we use a Maxpooling, ReLU, Fully connected layers method to segment the probabilities of cerebral micro bleeds and to apply an HOG and Statistical Features to extract the image. Compared to the previous methods, this can remove the massive redundant computations and increase the speed and detection of the process. In this proposed method sensitivity will be increased compared to the previous methods. Disease classification is also done due to cerebral Microbleeds by the support vector machine.
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