• Sindhuja.V
• Mythily.J
• Shobana Priya.H
• Dr. K.VALARMATHI, M.E., Ph.D

Abstract— One of the most popular applications of Artificial Intelligence that has seen an immense growth in the digital era is Machine Learning Techniques where the system studies and improves its performance through progressive learning without any explicit programming. Machine learning is widely used in numerous applications one of them being medical analysis. Feature extraction and Image classification are considered to be the most popularly used approaches done using machine learning process. In this paper, we have proposed an efficient model for detecting brain stroke by preprocessing CT/MRI scan images andfurther segmenting it for feature extraction. Preprocessing of the images are done using Medical Image Fusion that generates a high quality fused image with spectral and spatial information. The fused image is trained with SVM classifier to classify whether the image is benign or malignant. The efficiency of the proposed model is evaluated which produces 80.48% sensitivity, 99.9% specificity,and 99.69% accuracy. The performance of the model is more accurate when compared with traditional medical analysis as it uses a fused image having higher quality.

Keywords—Brain tumor, Classification, Accuracy, SVM,K-Means Clustering, Fusion, Medical Image Analysis.

"An Efficient Model For Brain Stroke Detection Using Machine Learning Techniques ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 2, page no.579-583, February-2019, Available :http://www.jetir.org/papers/JETIR1902674.pdf

"An Efficient Model For Brain Stroke Detection Using Machine Learning Techniques ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 2, page no. pp579-583, February-2019, Available at : http://www.jetir.org/papers/JETIR1902674.pdf