Prediction of Alzheimer's Disease Using Modified DNN with Optimal Feature Selection Based on Seagull Optimization.
Alzheimer's disease is a degenerative neurological condition resulting in brain cell death and brain tissue loss. Most importantly, memory-related brain cells are permanently harmed due to this condition. Alzheimer's disease diagnosis is a challenging task due to its high discriminative feature repr...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 2210 - 2229 |
|---|---|
| Autores principales: | , , , , |
| Formato: | algorithm diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2025
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187278946&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187278946 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2025 vid: 38 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187278946 187278946 187278946 10.1007/s10278-024-01262-z 187278946 ppf: 2210 ppct: 19 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Prediction of Alzheimer's Disease Using Modified DNN with Optimal Feature Selection Based on Seagull Optimization. aug: au: Bhansali, Ashok Sudheer, Devulapalli Tiwari, Shrikant Desanamukula, Venkata Subbaiah Ahmad, Faiyaz affil: https://ror.org/05fnxgv12 Dept of Computer Engineering and Applications, GLA University, Uttar Pradesh, 281406, Mathura, India sug: subj: Alzheimer's Disease Risk Factors Alzheimer's Disease Diagnosis Alzheimer's Disease Classification Convolutional Neural Networks Evaluation Risk Assessment Human Brain Radiography Magnetic Resonance Imaging Image Enhancement Sensitivity and Specificity Descriptive Statistics Precision Diagnostic Errors Surveys Machine Learning Algorithms Deep Learning Neuroradiography Neural Networks (Computer) Image Processing, Computer Assisted ab: Alzheimer's disease is a degenerative neurological condition resulting in brain cell death and brain tissue loss. Most importantly, memory-related brain cells are permanently harmed due to this condition. Alzheimer's disease diagnosis is a challenging task due to its high discriminative feature representation for classification using traditional machine learning (ML) methods. These challenges exist due to similar brain processes and pixel intensities. To overcome the above mentioned drawbacks, hybrid feature extraction techniques such as Gray Level Run Length Matrix (GLRLM), Gabor wavelet transform and Local Energy-based Shape Histogram (LESH) are used. In this designed model, Alzheimer's disease is predicted using brain MRI. At first, the collected magnetic resonance imaging (MRI) of the brain are resized and enhanced using the image resizing and BW-net technique. Features from these enhanced images are extracted using the GLRLM, Gabor wavelet transform and LESH techniques for shape, texture and edge of the brain MRI. Then, the extracted features are optimally selected using the SEAGULL optimization technique. These optimally selected features are trained using the modified DNN for predicting Alzheimer's disease. Performance metrics for proposed and existing models are studied and contrasted in order to assess the planned model. For the proposed model, 91%, 2%, 98% and 97% are performance metrics that were reached in aspects of precision, error, accuracy and recall. Thus, designed Alzheimer's disease prediction using modified DNN with optimal feature selection based on seagull optimization performs better and accurately predicts Alzheimer's disease. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|