• MOLLI CHONIKA
• PAIDI BHARAT KUMAR
• Adilla shekhar babu
• Chiru Venkata Ravi Kumar
• Kammila Venkata Mani Gopal

Brain tumor is a perilous disease which is brought on by abnormal growth of anomalous cells in the body which limits the functioning of brain. Brain tumors arises in a variety of size, features and shape. Identifying the brain tumor manually with error-prone is a difficult and time-consuming task for the radiologists as there is a good similarity between the normal tissues and brain tumor cells in appearance. In order to decrease the death rate or increase the survival rate of the effected people an early, efficient and accurate detection of brain tumor is required. Automated disease detection has become up-and-coming field in medical diagnostic applications. So, in this project brain tumor is detected automatically using convolutional neural network (CNN) based on Magnetic resonance imaging (MRI). MRI provides detailed information than other imaging techniques. This framework consists of five stages called as input layer, convolution layer, activation layer, pooling layer and fully connected layer. In the results, the loss and accuracy measured, predicted output and actual output are compared and confusion matrix is obtained.

Brain tumor, Convolutional neural network, Magnetic imaging resonance, input Layer, Convolution layer, activation layer, pooling layer and fully connected layer.

"Detection of Brain tumor based on Convolution Neural Network using MRI Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 10, page no.e119-e126, October-2022, Available :http://www.jetir.org/papers/JETIR2210417.pdf

"Detection of Brain tumor based on Convolution Neural Network using MRI Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 10, page no. ppe119-e126, October-2022, Available at : http://www.jetir.org/papers/JETIR2210417.pdf