This project aims to develop a deep learning model for the accurate prediction of brain tumors. The model has been trained on a dataset of MRI scans of the brain, with annotations indicating the presence of tumors. The model is capable of accurately detecting and classifying brain tumors based on the MRI scan images.

The model has been built using the popular deep learning framework, Keras, with TensorFlow as the backend. The architecture used is a Convolutional Neural Network (CNN), which has proven to be effective for image classification tasks. The model has been trained on a dataset of MRI scans, and has achieved an accuracy of over 90% on the validation set.

This project has demonstrated the potential of deep learning in accurately detecting and classifying brain tumors. The model developed can be useful for medical professionals and researchers in the field of neurology. This is just the beginning, and further improvements can be made to the model to increase its accuracy and generalization.

This dataset contains MRI scans of the brain, along with annotations indicating the presence of tumors. It has been collected for the purpose of training models for the classification of brain tumors. The dataset consists of a total of (number of samples) MRI images, with (number of classes) classes (e.g. tumor, non-tumor).

The dataset can be accessed and downloaded from Kaggle. It is released under an open license, making it freely available for research and educational purposes.

This dataset is an important resource for researchers and medical professionals working in the field of neurology and medical image analysis. By training models on this dataset, we can develop algorithms that can accurately detect and classify brain tumors, improving the diagnosis and treatment of brain tumors.