Detection and Classification of the Brain Tumor Brilliant Jumping Algorithm Optimized W-Net Enabled DCNN
Keywords:
Brain Tumor, Segmentation, W-Net, Brilliant Jumping Algorithm, and VGG-16Abstract
Brain tumors (BT) have remained a deadly disease across the world in recent years. Though there exist several detection mechanisms and models that provide the basic detection and classification of the tumor, they fail to work with certain parameters such as convergence, computational efficiency, and so on. To prevail over the issue of the conventional methods and to propose the detection as well as the classification model for grading the tumors is developed in this research. The Brilliant Jumping Algorithm Optimized W-Net Enabled Deep Convolutional Neural Network (BW-Net+BDN) is employed in the research of detecting and classifying BTs. The research included W-Net as the segmentation model that is optimized with the Brilliant jumping Algorithm (BJA). Furthermore, the included BJA optimization is the hybridization of the characteristics of the rabbit as well as the coyote to improve the effectiveness of the classifier, and the segmentation model as optimization is utilized in both classification and segmentation. The entire research detects the BT through the BW-Net segmentation model and classifies them based on grades such as normal, mild, and severe through the BDN classification model. The effectiveness of the BW-Net+BDN is analyzed with accuracy, Sensitivity, Specificity, and Balanced Error Rate that achieves 93.23%, 93.39%, 93.77%, and 6.58 respectively.
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