Enhancing Diagnosis: An Ensemble Deep Learning Model for Brain Tumor Detection and Classification

A brain tumor is a dangerous condition that can be challenging to reliably identify using conventional techniques, such as by looking at MRI scans. To solve this problem, our convolutional neural network (CNN) and transfer learning models were developed to distinguish between the three types of brain cancers that are most frequently found: gliomas, meningiomas, and pituitary tumors. The 7023 MRI scans of the human brain that make up our dataset were separated into four groups based on their tumor status: pituitary, glioma, meningioma, and no tumor. We used an ensemble method to combine pre-trained models and achieved exceptional accuracy in identifying the presence of a tumor, a combination of CNN and VGG16 with an accuracy of 0.97687 in validation data and up to 0.9801 in test data. Our findings showed how effectively and accurately our method classified brain cancers from MRI images. For a brain tumor to be successfully treated and to be life-saving, it must be identified as early as possible.