Dementia MRI image classification using transformation technique based on elephant herding optimization with Randomized Adam method for updating the hyper‐parameters - Bharanidharan, N; Rajaguru, Harikumar (International journal of imaging systems and technology, 2021)
The primary objective of this research work is to build a binary classifier for categorizing the input brain magnetic resonanceimaging (MRI) images as either demented or nondemented with high accuracy. A novel hyper‐parameter updating method called Randomized Adam (RanAdam) is proposed for enhancing the dementia classification accuracy of elephant herding optimization algorithm and other swarm intelligence (SI) algorithms. Usually, Adam method is widely used in deep learning neural networks for hyper‐parameters updating, and it is ingenious to use Adam and its modified version called RanAdam as hyper‐parameters updating method for SI algorithms. The proposed RanAdam algorithm tries to find actual optimal values for hyper‐parameters near the optimal values given by Adam method through the Controlled Randomness procedure. This research work also compares dementia MRI image classification performance of elephant herding optimization‐based transformation technique with the standard clustering approaches and other transformation approaches. In this research work, 117 subjects (65 non‐dementia and 52 dementia subjects) acquired from the Open Access Series of Imaging Studies (OASIS) database is used. Two cases are analyzed in all the techniques: with and without statistical features. The highest accuracy of 90.6% is achieved by elephant herding optimization (EHO)‐based transformation technique combined with RanAdam for updating hyper‐parameters for the case without statistical features. To verify the efficiency of the proposed technique, a popular Pima diabetic dataset is considered in addition to the OASIS dementia dataset and 88% accuracy is earned for EHO‐based transformation technique combined with RanAdam.