Adam bald eagle optimization-based Shepard CNN for classification and pixel change detection of brain tumour using post and pre-operative brain MRI images

Abstract

ABSTRACT Brain tumour is a dangerous disease and it harms health. This research develops a productive model to categorize brain tumours exploiting an Adam Bald Eagle optimization-based Shepard Convolutional Neural Network (ABEO-ShCNN). Initially, the preprocessing is done in pre- and post-operative Magnetic resonance imaging (MRI). Then, U-Net++ is exploited to segment, which is tuned by the Bald Border Collie Firefly Optimization Algorithm (BBCFO). The BBCFO is the incorporation of Border Collie Optimization (BCO), the Firefly optimization Algorithm (FA) and Bald Eagle Search (BES). Thereafter, feature extraction is done and then categorization is conducted using ShCNN in which the training is conducted by ABEO. The ABEO is the integration of Adam and BES. The ABEO-ShCNN model has acquired better accuracy, Positive Predictive Value (PPV), True Negative Rate (TNR), True Positive Rate (TPR) and Negative Predictive Value (NPV) for pre-operative MRI, with values of 92.70%, 92.90%, 91.30%, 89.60% and 89.50%, respectively.