چکیده (انگلیسی):

This study presents a novel method to classify the brain tumors by means of efficient and integrated methods
so as to increase the classification accuracy. In conventional systems, the problem being the same to extract the
feature sets from the database and classify tumors based on the features sets. The main idea in plethora of
earlier researches related to any classification method is to increase the classification accuracy.The actual need
is to achieve a better accuracy in classification, by extracting more relevant feature sets after dimensionality
reduction. There exists a trade-off between accuracy and the number of feature sets. Hence the focus in this
study is to implement Discrete Cosine Transform (DCT) on the brain tumor images for various classes. Using
DCT, by itself, it offers a fair dimension reduction in feature sets.Later on, sequentially K-means algorithm is
applied on DCT coefficients to cluster the feature sets. These cluster information are considered as refined
feature sets and classified using Support Vector Machine (SVM) is proposed in this study. This method of
using DCT helps to adjust and vary the performance of classification based on the count of the DCT
coefficients taken into account. There exists a good demand for an automatic classification of brain tumors
which grealtly helps in the process of diagnosis. In this novel work, an average of 97% and a maximum of
100% classification accuracy has been achieved. This research is basically aiming and opening a new way of
classification under compressed domain. Hence this study may be highly suitable for diagnosing under mobile
computing and internet based medical diagnosis.