Optimization of VGG16 utilizing the Arithmetic Optimization Algorithm for early detection of Alzheimer's disease. (April 2022) - Deepa, N.; Chokkalingam, S.P.
Highlights: During pre-processing, the CAT12 toolkit is used to process the format of a T1-weighted MRI image. The uneven light distribution is normalized thereby the image contrast level is enhanced via linear contrast stretching. An adaptive ROI model segments the brain image nodules. The segmentation model performance is impacted by adding redundant information into the ROIs. Abstract: Early detection and prevention of Alzheimer's disease (AD) is an important and challenging task. Determining a precise and accurate diagnosis of Alzheimer's disease in its early stages is the most significant challenge. As a result, various research for the early detection of Alzheimer's disease was conducted. However, these techniques have a number of drawbacks, including higher computational costs, failure to incorporate data from multiple modalities, performance degradation due to data distributions between training and testing data, inability to record brain affected regions, longer processing time, etc. To tackle these issues, we proposed Optimized VGG-16 architecture using Arithmetic Optimization Algorithm (Optimized VGG-16 using AOA) for AD classification. Three major components are involved in this study such as pre-processing, segmentation, and classification. The CAT12 toolkit is used to process the format of T1-weighted MRI images during pre-processing. The image enhancement techniques normalize the uneven light distribution in which the linear contrast stretching enhances the image contrast level. Finally, an Optimized VGG-16 using AOA effectively classifies the AD classes such as normal, mild dementia (severe cognitive decline), and late dementia (very severe cognitive decline) classes. The dataset images are chosen from Alzheimer's disease Neuroimaging Initiative (ADNI), the Open Access Series of Imaging Studies (OASIS) dataset, and Single Individual volunteer for Multiple Observations across Networks (SIMON) databases. The experimental investigations provided superior classification performances than other existing methods.