A Survey on Classification Algorithms of Brain Images in Alzheimer’s Disease Based on Feature Extraction Techniques
Alzheimer’s disease (AD) is one of the most serious neurological disorders for elderly people. AD affected patient experiences severe memory loss. One of the main reasons for memory loss in AD patients is atrophy in the hippocampus, amygdala, etc. Due to the enormous growth of AD patients...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9400359/ |
| _version_ | 1819295067554185216 |
|---|---|
| author | Ruhul Amin Hazarika Arnab Kumar Maji Samarendra Nath Sur Babu Sena Paul Debdatta Kandar |
| author_facet | Ruhul Amin Hazarika Arnab Kumar Maji Samarendra Nath Sur Babu Sena Paul Debdatta Kandar |
| author_sort | Ruhul Amin Hazarika |
| collection | DOAJ |
| description | Alzheimer’s disease (AD) is one of the most serious neurological disorders for elderly people. AD affected patient experiences severe memory loss. One of the main reasons for memory loss in AD patients is atrophy in the hippocampus, amygdala, etc. Due to the enormous growth of AD patients and the paucity of proper diagnostic tools, detection and classification of AD are considered as a challenging research area. Before a Cognitively normal (CN) person develops symptoms of AD, he may pass through an intermediate stage, commonly known as Mild Cognitive Impairment (MCI). MCI is having two stages, namely StableMCI (SMCI) and Progressive MCI (PMCI). In SMCI, a patient remains stable, whereas, in the case of PMCI, a person gradually develops few symptoms of AD. Several research works are in progress on the detection and classification of AD based on changes in the brain. In this paper, we have analyzed few existing state-of-art works for AD detection and classification, based on different feature extraction approaches. We have summarized the existing research articles with detailed observations. We have also compared the performance and research issues in each of the feature extraction mechanisms and observed that the AD classification using the wavelet transform-based feature extraction approaches might achieve convincing results. |
| first_indexed | 2024-12-24T04:36:19Z |
| format | Article |
| id | doaj.art-1f6e73a180e34146ae794c824456ed51 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-24T04:36:19Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-1f6e73a180e34146ae794c824456ed512022-12-21T17:15:10ZengIEEEIEEE Access2169-35362021-01-019585035853610.1109/ACCESS.2021.30725599400359A Survey on Classification Algorithms of Brain Images in Alzheimer’s Disease Based on Feature Extraction TechniquesRuhul Amin Hazarika0Arnab Kumar Maji1https://orcid.org/0000-0002-3320-9965Samarendra Nath Sur2https://orcid.org/0000-0001-8184-0623Babu Sena Paul3Debdatta Kandar4https://orcid.org/0000-0002-3409-5189Department of Information Technology, North Eastern Hill University, Shillong, IndiaDepartment of Information Technology, North Eastern Hill University, Shillong, IndiaDepartment of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Gangtok, IndiaInstitute for Intelligent Systems, University of Johannesburg, Johannesburg, South AfricaDepartment of Information Technology, North Eastern Hill University, Shillong, IndiaAlzheimer’s disease (AD) is one of the most serious neurological disorders for elderly people. AD affected patient experiences severe memory loss. One of the main reasons for memory loss in AD patients is atrophy in the hippocampus, amygdala, etc. Due to the enormous growth of AD patients and the paucity of proper diagnostic tools, detection and classification of AD are considered as a challenging research area. Before a Cognitively normal (CN) person develops symptoms of AD, he may pass through an intermediate stage, commonly known as Mild Cognitive Impairment (MCI). MCI is having two stages, namely StableMCI (SMCI) and Progressive MCI (PMCI). In SMCI, a patient remains stable, whereas, in the case of PMCI, a person gradually develops few symptoms of AD. Several research works are in progress on the detection and classification of AD based on changes in the brain. In this paper, we have analyzed few existing state-of-art works for AD detection and classification, based on different feature extraction approaches. We have summarized the existing research articles with detailed observations. We have also compared the performance and research issues in each of the feature extraction mechanisms and observed that the AD classification using the wavelet transform-based feature extraction approaches might achieve convincing results.https://ieeexplore.ieee.org/document/9400359/Alzheimer’s disease (AD)hippocampusmagnetic resonance imaging (MRI)mild cognitive impairment (MCI)progressive MCI (PMCI)stable MCI (SMCI) |
| spellingShingle | Ruhul Amin Hazarika Arnab Kumar Maji Samarendra Nath Sur Babu Sena Paul Debdatta Kandar A Survey on Classification Algorithms of Brain Images in Alzheimer’s Disease Based on Feature Extraction Techniques IEEE Access Alzheimer’s disease (AD) hippocampus magnetic resonance imaging (MRI) mild cognitive impairment (MCI) progressive MCI (PMCI) stable MCI (SMCI) |
| title | A Survey on Classification Algorithms of Brain Images in Alzheimer’s Disease Based on Feature Extraction Techniques |
| title_full | A Survey on Classification Algorithms of Brain Images in Alzheimer’s Disease Based on Feature Extraction Techniques |
| title_fullStr | A Survey on Classification Algorithms of Brain Images in Alzheimer’s Disease Based on Feature Extraction Techniques |
| title_full_unstemmed | A Survey on Classification Algorithms of Brain Images in Alzheimer’s Disease Based on Feature Extraction Techniques |
| title_short | A Survey on Classification Algorithms of Brain Images in Alzheimer’s Disease Based on Feature Extraction Techniques |
| title_sort | survey on classification algorithms of brain images in alzheimer x2019 s disease based on feature extraction techniques |
| topic | Alzheimer’s disease (AD) hippocampus magnetic resonance imaging (MRI) mild cognitive impairment (MCI) progressive MCI (PMCI) stable MCI (SMCI) |
| url | https://ieeexplore.ieee.org/document/9400359/ |
| work_keys_str_mv | AT ruhulaminhazarika asurveyonclassificationalgorithmsofbrainimagesinalzheimerx2019sdiseasebasedonfeatureextractiontechniques AT arnabkumarmaji asurveyonclassificationalgorithmsofbrainimagesinalzheimerx2019sdiseasebasedonfeatureextractiontechniques AT samarendranathsur asurveyonclassificationalgorithmsofbrainimagesinalzheimerx2019sdiseasebasedonfeatureextractiontechniques AT babusenapaul asurveyonclassificationalgorithmsofbrainimagesinalzheimerx2019sdiseasebasedonfeatureextractiontechniques AT debdattakandar asurveyonclassificationalgorithmsofbrainimagesinalzheimerx2019sdiseasebasedonfeatureextractiontechniques AT ruhulaminhazarika surveyonclassificationalgorithmsofbrainimagesinalzheimerx2019sdiseasebasedonfeatureextractiontechniques AT arnabkumarmaji surveyonclassificationalgorithmsofbrainimagesinalzheimerx2019sdiseasebasedonfeatureextractiontechniques AT samarendranathsur surveyonclassificationalgorithmsofbrainimagesinalzheimerx2019sdiseasebasedonfeatureextractiontechniques AT babusenapaul surveyonclassificationalgorithmsofbrainimagesinalzheimerx2019sdiseasebasedonfeatureextractiontechniques AT debdattakandar surveyonclassificationalgorithmsofbrainimagesinalzheimerx2019sdiseasebasedonfeatureextractiontechniques |