Hybrid Whale and Gray Wolf Deep Learning Optimization Algorithm for Prediction of Alzheimer’s Disease

Abstract

In recent years, finding the optimal solution for image segmentation has become more important in many applications. The whale optimization algorithm (WOA) is a metaheuristic optimization technique that has the advantage of achieving the global optimal solution while also being simple to implement and solving many real-time problems. If the complexity of the problem increases, the WOA may stick to local optima rather than global optima. This could be an issue in obtaining a better optimal solution. For this reason, this paper recommends a hybrid algorithm that is based on a mixture of the WOA and gray wolf optimization (GWO) for segmenting the brain sub regions, such as the gray matter (GM), white matter (WM), ventricle, corpus callosum (CC), and hippocampus (HC). This hybrid mixture consists of two steps, i.e., the WOA and GWO. The proposed method helps in diagnosing Alzheimer’s disease (AD) by segmenting the brain sub regions (SRs) by using a hybrid of the WOA and GWO (H-WOA-GWO, which is represented as HWGO). The segmented region was validated with different measures, and it shows better accuracy results of 92%. Following segmentation, the deep learning classifier was utilized to categorize normal and AD images. The combination of WOA and GWO yields an accuracy of 90%. As a result, it was discovered that the suggested method is a highly successful technique for identifying the ideal solution, and it is paired with a deep learning algorithm for classification.

CO‐WOA: Novel Optimization Approach for Deep Learning Classification of Fish Image

“…The typical VGG‐19 design now includes a pooling layer, a dense layer with 1024 nodes, and RelU based activation function. In addition, the architecture has an ultimate layer with 9 nodes representing the nine species [48,49] …”

“…In addition, the architecture has an ultimate layer with 9 nodes representing the nine species. [48,49] DenseNet 121 Model DenseNet has four average pooling layers in addition to 120 convolutional layers. By simplifying the connection structure between layers and changing the conventional CNN architecture, the Model is able to resolve the Vanishing Gradient issue.…”

Prediction of Alzheimer's disease stages based on ResNet-Self-attention architecture with Bayesian optimization and best features selection

“…Due to the brain's intricacy, classifying AD is difficult (Dhakhinamoorthy et al, 2023 ). Thus, researchers are improving medical image processing to identify AD correctly.…”

“…Few of them are related to insufficient and unbalanced datasets. Furthermore, major problems with AD patients are the complexity, diversity, and complicated neurobiological underlying AD (Dhakhinamoorthy et al, 2023 ). Architectural variation in scans is another main challenge to diagnosing and detecting AD.…”

Gri̇ Kurt Opti̇mi̇zasyonunda Kurt Li̇deri̇ni̇n Geli̇şti̇ri̇lmesi̇

“…The experimental findings demonstrate that IWOA not only performs better in optimization while solving benchmark functions of various dimensions but also when IKNN is utilized for feature selection, IWOAIKFS performs better in classification. In order to segment the brain's subregions, a hybrid method that combines the WOA and GWO is suggested in [18]. The suggested approach aids in the diagnosis of Alzheimer's disease.…”