Download Advances in Intelligent Informatics (Advances in Intelligent by Sabu M. Thampi, El-Sayed M. El-Alfy, Hideyuki Takagi, Selwyn PDF

By Sabu M. Thampi, El-Sayed M. El-Alfy, Hideyuki Takagi, Selwyn Piramuthu, Thomas Hanne

This e-book features a choice of refereed and revised papers of clever Informatics tune initially awarded on the 3rd foreign Symposium on clever Informatics (ISI-2014), September 24-27, 2014, Delhi, India. The papers chosen for this tune hide numerous clever informatics and similar issues together with sign processing, trend acceptance, photograph processing, facts mining and their purposes.

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Extra resources for Advances in Intelligent Informatics (Advances in Intelligent Systems and Computing, Volume 320)

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During the training phase the SVM classifier is trained with a training data set which contains feature vectors extracted from the training images and their respective class labels. During the testing phase if an unknown image’s feature vector is given as an input to the trained classifier, it classifies the test image as belonging to one of classes [18]. Experiments are conducted with support vector machine classifier using quadratic kernel function and RBF kernel function. 1 Experimental Results Data Set Used The images for this work are taken from Cancer imaging archive [22].

S. Natteshan · J. edu © Springer International Publishing Switzerland 2015 El-Sayed M. El-Alfy et al. S. Natteshan and J. Angel Arul Jothi Introduction Brain tumor is a solid neoplasm of uncontrolled cell division. CAD is a process of using computation capacity effectively to diagnose a condition from medical images or other medical related data. CAD is mainly used to provide a second opinion thereby helping doctors while performing diagnosis. Image processing is a technique where the input is an image and the output being certain parameters related to image.

During the training phase, the classifier is trained with features from training images. In testing phase, an unknown image’s features are given to the classifier and it has to classify the image as ‘tumor affected’ or ‘tumor not affected’. In this project two classifier models are used namely Support vector machine and neural network. S. Natteshan and J. 1 Neural Network Classifier A Neural network classifier mimics the processing ability of biological brain. They are mainly divided into two types namely feed forward network and recurrent or feedback network.

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