• Dr C.Berin Jones
• C. Murugamani

In this modern era the clinical laboratory have greater attention to produce an accurate result for every test particularly in the area of Brain tumor. The brain tumor is very essential to detect as well as to follow the treatment of many diseases like benign, malignant etc. For the identification of brain tumor three phases are used. First phase is the segmentation and the segmentation used here is the threshold based segmentation. While using the threshold based segmentation we get better result when compared to the previous method. Second phase is the feature extraction here the feature is extracted using the GLCM feature. And the third or final phase is the classification. Here three classifiers are classified they are Support Vector Machine classifier, Adaboost classifier and Naive Bayes classifier. By using the above three classifiers Naive Bayes classifier gives high accuracy i.e.99%. The simulations are done on MATLAB application.

Adaboost, Bilateral filtering, Brain tumor, Naïve Bayes classifier, SVM

"Bilateral filtering with cosine transform based brain tumor classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 4, page no.360-371, April-2019, Available :http://www.jetir.org/papers/JETIR1904856.pdf

"Bilateral filtering with cosine transform based brain tumor classification", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 4, page no. pp360-371, April-2019, Available at : http://www.jetir.org/papers/JETIR1904856.pdf