Classification and Dichotomy Case Study

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Features are essential for any classification or analysis in image processing. There are many types of features which can be extracted from the images each gives its identical informations about the image. Here MA region supposed with properties such as shape, color and size which appears as a dark red colored circle shape. To identify MA and non- MA region feature vectors are formed for each candidate regions.
The classification is the final process which classifies the result (I.e, Normal, Abnormal etc). There are various classifiers used in literature which divides into two classes majorly called as dichotomies and some classifies into multi classes (e.g. decision trees [17], feedforward neural networks).
Support Vector Machine (SVM) is a useful method for classification of high dimenisional problems which suits for only 2 class classification. For multi class classification (K) the classifier has to be trained typically placed in parallel and each one of them is trained to separate one class from the K - 1 others. This way of decomposing a general classification problem into dichotomies is known as a one-per-class decomposition, and is independent of the learning method used to train the classifiers. This process is little difficult and lacks in time consumption.
Thus a multi class classification of SVM is choosen here for the classification from Cody Neuburger [18]. In traditional SVM; the structure of trained SVM is formed in a 1×1 structure. And from that structure

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