Feature selection is the important technique which plays a pivotal part in process of data mining. This is mainly needed to deal with the extreme number of features, normally which can turn into a computational over burden on the process of learning algorithms. Such type of algorithm is also necessary, even when the computational resources are not panic, since it improves the accuracy of the machine learning tasks. In the field of medical science there are various kinds of problem in the area of medical imaging as like extraction, classification, segmentation, selection and etc. On the other side Medical datasets are normally characterized by gigantic extent of disease dimensions and relatively little quantity of patient records. Such feature selection is not appropriate, where all such immaterial and redundancy features are extremely typical to estimate. On the contrary, many features might cause the issue of memory storage to characterize a data set. In such a situation various data mining algorithms or techniques might be implemented for dealing with such a huge amount of data with precision. This paper basically represents a brief review about the medical image feature selection for the diagnosis of the tumor by help of data mining methods.
Volume 11 | Issue 11
Pages: 76-85
DOI: 10.5373/JARDCS/V11I11/20193171