Categorization of Alzheimer’s disease stages using deep learning approaches with McNemar’s test
Early diagnosis is crucial in Alzheimer’s disease both clinically and for preventing the rapid progression of the disease. Early diagnosis with awareness studies of the disease is of great importance in terms of controlling the disease at an early stage. Additionally, early detection can reduce trea...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-02-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-1877.pdf |
| _version_ | 1797299097329205248 |
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| author | Begüm Şener Koray Acici Emre Sümer |
| author_facet | Begüm Şener Koray Acici Emre Sümer |
| author_sort | Begüm Şener |
| collection | DOAJ |
| description | Early diagnosis is crucial in Alzheimer’s disease both clinically and for preventing the rapid progression of the disease. Early diagnosis with awareness studies of the disease is of great importance in terms of controlling the disease at an early stage. Additionally, early detection can reduce treatment costs associated with the disease. A study has been carried out on this subject to have the great importance of detecting Alzheimer’s disease at a mild stage and being able to grade the disease correctly. This study’s dataset consisting of MRI images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was split into training and testing sets, and deep learning-based approaches were used to obtain results. The dataset consists of three classes: Alzheimer’s disease (AD), Cognitive Normal (CN), and Mild Cognitive Impairment (MCI). The achieved results showed an accuracy of 98.94% for CN vs AD in the one vs one (1 vs 1) classification with the EfficientNetB0 model and 99.58% for AD vs CNMCI in the one vs All (1 vs All) classification with AlexNet model. In addition, in the study, an accuracy of 98.42% was obtained with the EfficientNet121 model in MCI vs CN classification. These results indicate the significant potential for mild stage Alzheimer’s disease detection of Alzheimer’s disease. Early detection of the disease in the mild stage is a critical factor in preventing the progression of Alzheimer’s disease. In addition, a variant of the non-parametric statistical McNemar’s Test was applied to determine the statistical significance of the results obtained in the study. Statistical significance of 1 vs 1 and 1 vs all classifications were obtained for EfficientNetB0, DenseNet, and AlexNet models. |
| first_indexed | 2024-03-07T22:44:37Z |
| format | Article |
| id | doaj.art-751da41c81014296a15f027660272498 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-03-07T22:44:37Z |
| publishDate | 2024-02-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-751da41c81014296a15f0276602724982024-02-23T15:05:21ZengPeerJ Inc.PeerJ Computer Science2376-59922024-02-0110e187710.7717/peerj-cs.1877Categorization of Alzheimer’s disease stages using deep learning approaches with McNemar’s testBegüm Şener0Koray Acici1Emre Sümer2Department of Computer Engineering, Başkent University, Ankara, Başkent University, Ankara, TurkeyDepartment of Artificial Intelligence and Data Engineering, Ankara University, Ankara University, Ankara, TurkeyDepartment of Computer Engineering, Başkent University, Ankara, Başkent University, Ankara, TurkeyEarly diagnosis is crucial in Alzheimer’s disease both clinically and for preventing the rapid progression of the disease. Early diagnosis with awareness studies of the disease is of great importance in terms of controlling the disease at an early stage. Additionally, early detection can reduce treatment costs associated with the disease. A study has been carried out on this subject to have the great importance of detecting Alzheimer’s disease at a mild stage and being able to grade the disease correctly. This study’s dataset consisting of MRI images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) was split into training and testing sets, and deep learning-based approaches were used to obtain results. The dataset consists of three classes: Alzheimer’s disease (AD), Cognitive Normal (CN), and Mild Cognitive Impairment (MCI). The achieved results showed an accuracy of 98.94% for CN vs AD in the one vs one (1 vs 1) classification with the EfficientNetB0 model and 99.58% for AD vs CNMCI in the one vs All (1 vs All) classification with AlexNet model. In addition, in the study, an accuracy of 98.42% was obtained with the EfficientNet121 model in MCI vs CN classification. These results indicate the significant potential for mild stage Alzheimer’s disease detection of Alzheimer’s disease. Early detection of the disease in the mild stage is a critical factor in preventing the progression of Alzheimer’s disease. In addition, a variant of the non-parametric statistical McNemar’s Test was applied to determine the statistical significance of the results obtained in the study. Statistical significance of 1 vs 1 and 1 vs all classifications were obtained for EfficientNetB0, DenseNet, and AlexNet models.https://peerj.com/articles/cs-1877.pdfAlzheimer’s diseaseDeep learningClassificationEarly diagnosisMcNemar’s test |
| spellingShingle | Begüm Şener Koray Acici Emre Sümer Categorization of Alzheimer’s disease stages using deep learning approaches with McNemar’s test PeerJ Computer Science Alzheimer’s disease Deep learning Classification Early diagnosis McNemar’s test |
| title | Categorization of Alzheimer’s disease stages using deep learning approaches with McNemar’s test |
| title_full | Categorization of Alzheimer’s disease stages using deep learning approaches with McNemar’s test |
| title_fullStr | Categorization of Alzheimer’s disease stages using deep learning approaches with McNemar’s test |
| title_full_unstemmed | Categorization of Alzheimer’s disease stages using deep learning approaches with McNemar’s test |
| title_short | Categorization of Alzheimer’s disease stages using deep learning approaches with McNemar’s test |
| title_sort | categorization of alzheimer s disease stages using deep learning approaches with mcnemar s test |
| topic | Alzheimer’s disease Deep learning Classification Early diagnosis McNemar’s test |
| url | https://peerj.com/articles/cs-1877.pdf |
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