An accurate and efficient multi-task brain tumour detection with segmented MRI images using auto-metric adolescent neural network

Early diagnosis of a brain tumour (BT) boosts that the patient will survive after medication. Several existing methods for detecting BTs are intrusive, cumbersome, and vulnerable to human errors. This manuscript introduces a novel hybrid method, auto-metric graph adolescent identity neural network (AGAINN), for accurate and efficient human BT segmentation and multi-task detection using magnetic resonance imaging (MRI) images. The input brain MRI images are given to structural interval gradient filtering (SIGF) based preprocessing method for eliminating noise, resizing and increasing the excellence of brain images and then provided into adaptive transfer density peaks search (ATDPS) clustering based segmentation for finding the region of interest (RoI) of the preprocessed image. Then, three types of feature extraction are done using empirical wavelet transform (EWT) and grey-level co-occurrence matrix (GLCM). The extracted image features are transferred into the suggested scheme for detecting tumour and also the types of tumour also performance analyses are compared using several metrics, statistical tests and improved accuracy rate.