Optimizing Brain Tumor Detection with Multi-Head Deep Convolutional Neural Network: An Effective Framework for MRI Based Classification and Analysis

Various detection techniques are used to find brain tumors, but MRI is the most reliable because of its high image resolution. However, classifying brain tumors manually takes a lot of time. This study suggests a novel method for classifying brain tumors called the Multi-head Deep Convolutional Neural Network (MHDCovNet) Algorithm in order to overcome these difficulties. The Data Pre-processing phase includes improvements like noise reduction, image sharpening, and the removal of non-brain tissues (skull stripping) to guarantee the images are of high quality appropriate for analysis. The above steps are essential for getting images ready for further examination. Important attributes, such as statistical and texture-based features, are extracted from the pre-processed MRI scans in the Feature Extraction phase in order to obtain the information required for classification. A complex Multi-head Deep Convolutional Neural Network (MHDCovNet), which makes use of cutting- edge deep learning features like multi-head attention and global average pooling, is used to achieve the final phase, tumor classification. The effectiveness of the developed technique in classifying brain tumors is then demonstrated by comparing the results with state of the art methods.