Brain Tumor Classification Dataset
Overview This dataset is curated for the task of brain tumor classification using magnetic resonance imaging (MRI) scans. The dataset is intended to facilitate research and development of machine learning models, particularly Convolutional Neural Networks (CNNs), for distinguishing between various types of brain tumors and normal brain tissue. The data contains labeled MRI images across four categories: glioma, meningioma, pituitary tumors, and no tumor (normal).
Dataset Structure The dataset is divided into two main sets: Training Data: Used for training machine learning models. Testing Data: Used for evaluating the performance of trained models. Both the training and testing datasets contain MRI scans labeled across the following four classes:
Glioma: Tumors that originate in the glial cells of the brain or spine. These are typically aggressive and require immediate treatment. Meningioma: Tumors that develop from the meninges, the protective membranes covering the brain and spinal cord. Although usually benign, their location can cause significant neurological effects. Pituitary Tumor: Tumors located in the pituitary gland, which can affect hormone production and cause systemic symptoms. No Tumor (Normal): MRI images showing healthy brain tissue without any abnormal growths or tumors.
Data Format File Type: JPEG/PNG MRI scans. Classes: Glioma Meningioma Pituitary Tumor No Tumor (Normal) Each image is labeled according to the type of tumor it represents, or if it contains no tumor at all.