Brain Neoplasm Identification using CNN with VGG-16 Model

In medical imaging, brain neoplasm identification is essential since a timely diagnosis often enhances patient outcomes. the study focuses on utilizing the VGG-16 model, a widely acknowledged convolutional neural network computer vision, for detection of brain tumor. The primary objectives include classifying magnetic resonance imaging (MRI) images and accurately identifying the presence of cerebral tumor. The dataset utilized comprises MRI images. The methodology encompasses setting up the environment, data importation and preprocessing, VGG-16 model creation, and assessing its performance in terms of accuracy, precision, and recall, The results indicate that the VGG-16 model holds promise in aiding medical professionals in brain diagnosis, achieving an accuracy of approximately 95% on the validation set and 82% on the test set.