Long Short-Term Memory and Gated Recurrent Unit Based Brain Tumor Detection

Early detection is crucial in identifying brain cancers, as they can lead to serious and incurable illnesses if left Background. Accurate identification of brain tumors is essential in determining the appropriate treatment, which significantly increases the chances of survival for patients. This research work is driven by the high demand and necessity for understanding brain tumors. Our proposed methodology utilizes brain tumor MRI images from an open-source repository. The identification of brain tumors involves four methods: image processing, segmentation, feature extraction, and classification. There are various methods currently used for classifying and segmenting brain tumors. Our hybrid classification algorithm, which combines Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU), compares trained and test data to provide accurate results. The use of a large dataset is crucial in achieving accurate predictions. These predictions assist radiologists in making prompt decisions and implementing appropriate treatment plans by detecting brain cancers early on