• Pragya Tiwari
• Priya mital

Biomedical technology is crucial in the identification and treatment for life-threatening illnesses. Brain tumours (BT) has recently risen to the top of the list of the most common and severe diseases. The ability of doctors to treat brain tumours is based on their knowledge and expertise. As a result, an automated mri brain system is essential for radiologists and doctors to assist in the detection of brain tumours. Honest assessment of magnetic resonance (MRI) data is necessary to detect brain tumours, and this study gives an effective method for predicting brain malignancies. VGG19-Net, Methods include data augmentation and image pre-processing algorithms that detect extreme shapes and trim black borders from photos. The photographs are then resized to the appropriate size. A ReLU Optimizer has indeed been implemented into our network to speed up the training. To attain maximum accuracy, the MRI image dataset is used to train & test the model. With 0.91 accuracy, the efficiency of the optimized system is evaluated.

MRI, CNN, K-PCA, Brain Tumour detection, VGG19-Net.

"MRI-Based Prediction for Brain Malignancies VGG19-Net", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.b324-b331, June-2022, Available :http://www.jetir.org/papers/JETIR2206145.pdf

"MRI-Based Prediction for Brain Malignancies VGG19-Net", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. ppb324-b331, June-2022, Available at : http://www.jetir.org/papers/JETIR2206145.pdf