Automated classification of MRI images for mild cognitive impairment and Alzheimer’s disease detection using log gabor filters based deep meta-heuristic classification
Affiliations: [a] Department of Computer Science and Engineering, Tamilnadu College of Engineering, Karumathampatti, Coimbatore, Tamilnadu, India
| [b] Department of Computer Science and Business Systems, Sri Krishna College of Engineering and Technology, Kuniamuthur, Coimbatore, Tamilnadu, India
| [c] Department of Computer Science and Engineering, Gayatri Vidya Parishad College of Engineering (A), Madhurawada, Andhra Pradesh, Tamilnadu, India
| [d] CSE, SNSCT
Correspondence:
[*]
Corresponding author. Dr. A.S. Shanthi, Department of Computer Science and Engineering, Tamilnadu College of Engineering, Karumathampatti, Coimbatore-641659, Tamilnadu, India. E-mail: [email protected].
Abstract: In older people, mild cognitive impairment (MCI) is a precursor to more severe forms of dementia like AD (AD). In diagnosing patients with primary AD and amnestic MCI, modern neuroimaging techniques, especially MRI, play a key role. To efficiently categorize MRI images as normal or abnormal, the research presents a machine learning-based automatic labelling system, with a focus on boosting performance via texture feature analysis. To this end, the research implements a preprocessing phase employing Log Gabor filters, which are particularly well-suited for spatial frequency analysis. In addition, the research uses Gray Wolf Optimization (GWO) to acquire useful information from the images. For classification tasks using the MRI images, the research also make use of DenseNets, a form of deep neural network. The proposed method leverages Log Gabor filters for preprocessing, Gray Wolf Optimization (GWO) for feature extraction, and DenseNets for classification, resulting in a robust approach for categorizing MRI images as normal or abnormal. When compared to earlier trials performed without optimization, the proposed systematic technique shows a significant increase in classification accuracy of 15%. For neuroimaging applications, our research emphasizes the use of Log Gabor filters for preprocessing, GWO for feature extraction, and DenseNets for classification, which can help with the early detection and diagnosis of MCI and AD.
