• Ms.K.A.Kulkarni
• Mr. Krushil M. Punwatkar

Digital image processing can help medical expertise in detection and classify the tumour in normal and abnormal type. This paper focus on classifiers as support vector machine, k-nearest neighbour and neural network for classification and confusion matrix for performance evaluation of brain MRI tumour and non-tumour images .Among different segmentation techniques, Otsu’s thresholding method is adopted for segmentation purpose. Gray level co-occurrence matrix (GLCM) is used for the feature extraction. In this study ,classification methods gives desired output in terms of confusion matrix parameters which can be used for evaluating performance of the classifier in terms of accuracy ,sensitivity, precision and F1 score.

artificial neural network (ANN) , confusion matrix , feature extraction, gray level co-occurrence matrix (GLCM), image segmentation, receiver operating characteristic (ROC)

"Classification And Performance Evaluation Of Brain MRI Images Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 7, page no.g665-g672, July-2022, Available :http://www.jetir.org/papers/JETIR2207687.pdf

"Classification And Performance Evaluation Of Brain MRI Images Using Machine Learning Algorithms", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 7, page no. ppg665-g672, July-2022, Available at : http://www.jetir.org/papers/JETIR2207687.pdf