Machine Learning and Image Processing Enabled Evolutionary Framework for Brain MRI Analysis for Alzheimer’s Disease Detection

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 8
• Main Authors: Kamal, Mustafa, Pratap, A. Raghuvira, Naved, Mohd, Zamani, Abu Sarwar, Nancy, P., Ritonga, Mahyudin, Shukla, Surendra Kumar, Sammy, F.
• Format: Journal Article
• Language: English
• Published: United States Hindawi 2022; John Wiley & Sons, Inc
• Subjects: Accuracy; Algorithms; Alzheimer Disease - diagnostic imaging; Alzheimer's disease; Artificial intelligence; Aversion; Brain; Brain - diagnostic imaging; Brain research; Classification; Computational neuroscience; Decision making; Equalization; Feature extraction; Histograms; Humans; Image enhancement; Image processing; Image Processing, Computer-Assisted - methods; Image quality; Learning algorithms; Literature reviews; Machine Learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical diagnosis; Medical imaging; Medical imaging equipment; Medical research; Medicine, Experimental; Memory; Nerves; Neurodegenerative diseases; Noise; Older people; Parameter sensitivity; Proteins; Support Vector Machine; Support vector machines; Wavelet transforms; India
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2022/5261942

Alzheimer’s disease is characterized by the presence of abnormal protein bundles in the brain tissue, but experts are not yet sure what is causing the condition. To find a cure or aversion, researchers need to know more than just that there are protein differences from the usual; they also need to know how these brain nerves form so that a remedy may be discovered. Machine learning is the study of computational approaches for enhancing performance on a specific task through the process of learning. This article presents an Alzheimer’s disease detection framework consisting of image denoising of an MRI input data set using an adaptive mean filter, preprocessing using histogram equalization, and feature extraction by Haar wavelet transform. Classification is performed using LS-SVM-RBF, SVM, KNN, and random forest classifier. An adaptive mean filter removes noise from the existing MRI images. Image quality is enhanced by histogram equalization. Experimental results are compared using parameters such as accuracy, sensitivity, specificity, precision, and recall.