• Jagpreet Kour Dariya
• Rupatai Lichode

Abstract—Adaptive brain tumor detection, Image processing and supervised machine learning concepts are used as the medical tools for detection of tumor. In this paper, we have used watershed segmentation and also we have also used object extraction for more detailed information of tumor region. To make this system more adaptive we used CNN (convolutional neural network), CNN which is a part of supervised machine learning is used here for analyzing and classification of the pattern for future use. Also, for patterns we have to find out the feature to train CNN, through the categorical dataset .Here we have gone through the Cross validation technique to evaluate generalization for the performance of learning algorithm. For that here we have gone through the different layers of CNN such as local connectivity, weight sharing and pooling. It is expected that the experimental results of the proposed system will give better result in comparison to other existing systems

Watershed segmentation, CNN, Pooling, Fully connected, Cross Validation.

"An Approach For Brain Tumor Detection Using Convolutional Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.784-790, June-2019, Available :http://www.jetir.org/papers/JETIR1906118.pdf

"An Approach For Brain Tumor Detection Using Convolutional Neural Network", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp784-790, June-2019, Available at : http://www.jetir.org/papers/JETIR1906118.pdf