Classification and Pixel Change Detection of Brain Tumor Using Adam Kookaburra Optimization-Based Shepard Convolutional Neural Network.

Abstract

The uncommon growth of cells in the brain is termed as brain tumor. To identify chronic nerve problems, like strokes, brain tumors, multiple sclerosis, and dementia, brain magnetic resonance imaging (MRI) is normally utilized. Identifying the tumor on early stage can improve the patient's survival rate. However, it is difficult to identify the exact tumor region with less computational complexity. Also, the tumors can vary significantly in shape, size, and appearance, which complicates the task of correctly classifying tumor types and detecting subtle pixel changes over time. Hence, an Adam kookaburra optimization-based Shepard convolutional neural network (AKO-based Shepard CNN) is established in this study for the classification and pixel change detection of brain tumor. The Adam kookaburra optimization (AKO) is established by integrating the kookaburra optimization algorithm (KOA) and Adam. Here, the pre- and post-operative MRIs are pre-processed and then segmented by U-Net++. The tuning of U-Net++ is done by the bald Border collie firefly optimization algorithm (BBCFO). The bald eagle search (BES), firefly algorithm (FA), and Border collie optimization (BCO) are combined to form the BBCFO. The next operation is the feature extraction and the classification is conducted at last using ShCNN. The AKO is utilized to tune the ShCNN for obtaining effective classification results. Unlike conventional optimization algorithms, AKO offers faster convergence and higher accuracy in classification. The highest negative predictive value (NPV), true negative rate (TNR), true positive rate (TPR), positive predictive value (PPV), and accuracy produced by the AKO-based ShCNN are 89.91%, 92.26%, 93.78%, and 93.60%, respectively, using Brain Images of Tumors for Evaluation database (BITE).