M Azad, N Karthik, R Abhinay, E Ravi Kondal and V Kakulapati

Sreenidhi Institute of Science and Technology, Yamnampet, Hyderabad, Telangana, India

*Corresponding Author: V Kakulapati, Sreenidhi Institute of Science and Technology, Yamnampet, Hyderabad, Telangana, India.

Received: April 03, 2023; Published: May 25, 2023

Abstract

Alzheimer's recognition and elimination is a challenging problem in the medical field. Before advanced imaging techniques like CT scans and MRI scans were available, invasive methods such as pneumo- encephalography and cerebral angiography were used. These methods have since been replaced by non-invasive imaging techniques, which provide improved visual information for surgeons. The three-step technique described for Alzheimer's disease identification main factor that influences, fragmentation, and morphology functionality of the images. Once the input picture has been grayscale for pre-processing, a high-pass filter is used to reduce noise and a median filter is applied to enhance image quality. Tumor characteristics are extracted using the wavelet transform, and the dimensionality of those features is reduced using PCA (principal component analysis). A kernel support vector machine is then used to assess the trimmed features (KSVM). K-fold cross-verification is utilized to further enhance the KSVM's effectiveness.

Keywords: Brain; Alzheimer; Disease; Thresholding; Morphological Operations; Kernel; PCA; SVM

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Citation: V Kakulapati., et al. “A Novel Method of Early Detection of Ad (Alzheimer's Disease)". Acta Scientific Computer Sciences 5.6 (2023): 36-41.

Copyright

Copyright: © 2023 V Kakulapati., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.