• P.Gayatri
• D.Anirudh
• K.Kiran Kumar
• M.Jagadeesh Srinivas
• Dr.E.Laxmi Lydia

In recent years, Brain tumour has caused a tremendous impact on the mortality rate of humans. It happened due to inefficient methodologies. Brain tumour is the development of unhealthy cells in brain which may leads to cancer. These unhealthy cells in the brain are identified with the help of MRI and CT scan images. So to decrease the mortality rate of humans, we proposed convolution neural networks model to detect tumour in brain. This convolution neural network model is integrated with transfer learning techniques such as VGG-16 to identify the tumour in brain. Transfer Learning is used to extract the features from brain MRI images to improve the efficiency of the model. This model will determine whether an individual person is having a tumour or not. The proposed model helps us to detect the tumour in earlier stages so that it can prevent the mortality rate of humans. So this approach is developed as it would save radiologist time, reduce human efforts, decrease human faults and obtain a best accuracy results.

Brain Tumour, Magnetic Resonance Imaging (MRI), Convolution Neural Networks, Accuracy, VGG-16

"BRAIN TUMOUR CLASSIFICATION USING CONVOLUTION NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.c653-c658, June-2022, Available :http://www.jetir.org/papers/JETIR2206281.pdf

"BRAIN TUMOUR CLASSIFICATION USING CONVOLUTION NEURAL NETWORKS", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppc653-c658, June-2022, Available at : http://www.jetir.org/papers/JETIR2206281.pdf