A Novel DL Structure for Brain Tumor Identification Using MRI Images

The multimodal MRI scans described in this article are used to categorize brain tumors based on their location and size. Brain tumors need to be categorized in order to assess the tumors and choose the appropriate course of treatment for each class. Many different imaging methods are used to detect brain tumors. However, because MRI does not use ionizing radiation and generates better images, it is commonly used. Using deep learning (DL), a branch of machine learning has recently demonstrated impressive results, particularly in segmentation and classifiable tasks. This paper proposes a convolutional neural network-based deep learning model (DL) that uses transfer learning and EfficientNet to classify various kinds of brain cancers using publically accessible datasets. The first divides cancers into three categories: glioma, meningioma, and pituitary tumor. Compared to conventional deep learning techniques, the suggested approach produces superior results. The Python platform can be used to complete the task.