Hybrid Adam Sewing Training Optimization Enabled Deep Learning for Brain Tumor Segmentation and Classification using MRI Images

Abstract

ABSTRACT A brain tumour (BT) is a growth of tissue that is organised by a gradual accumulation of anomalous cells, and it is significant to segment and classify the BT from magnetic resonance imaging (MRI) for treatment. The manual investigation of brain MRI tumour classification and recognition is the normal technique; however, the result produced in this manner is expensive and inaccurate. Hence, this research invents the novel BT segmentation and classification techniques, where Adam Sewing Training Based Optimization with the UNet++ (AdamSTBO+UNet++) performs the segmentation task, and Adam Salp Water Wave Optimisation with the Deep Convolutional Neural Network (AdamSWO-DCNN) performs the classification task. Here, AdamSTBO is generated by adapting the concept of Adam optimiser with the upgrade function of Sewing Training Based Optimisation (STBO) algorithm. The experimental result provides that the AdamSTBO+UNet++ for BT segmentation attained a dice coefficient of 0.909, which is higher than the existing BT segmentation techniques. Likewise, using the novel BT classification technique in this research, AdamSWO-DCNN got the accuracy, Negative Predictive Value (NPV), Positive Predictive Value (PPV), True Negative Rate (TNR), and True Positive Rate (TPR) of 0.928, 0.918, 0.925, 0.929, and 0.929.