A<sup>3</sup>C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer
Alzheimer’s disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer’s disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, disorientation, mood swings, bodily functions, memor...
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MDPI AG
2022-06-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/11/4250 |
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| author | Nadiah A. Baghdadi Amer Malki Hossam Magdy Balaha Mahmoud Badawy Mostafa Elhosseini |
| author_facet | Nadiah A. Baghdadi Amer Malki Hossam Magdy Balaha Mahmoud Badawy Mostafa Elhosseini |
| author_sort | Nadiah A. Baghdadi |
| collection | DOAJ |
| description | Alzheimer’s disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer’s disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, mood or personality changes, and ultimately death due to dementia. Unfortunately, no cure has yet been developed for it, and it has no known causes. Clinically, imaging tools can aid in the diagnosis, and deep learning has recently emerged as an important component of these tools. Deep learning requires little or no image preprocessing and can infer an optimal data representation from raw images without prior feature selection. As a result, they produce a more objective and less biased process. The performance of a convolutional neural network (CNN) is primarily affected by the hyperparameters chosen and the dataset used. A deep learning model for classifying Alzheimer’s patients has been developed using transfer learning and optimized by Gorilla Troops for early diagnosis. This study proposes the A<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>3</mn></msup></semantics></math></inline-formula>C-TL-GTO framework for MRI image classification and AD detection. The A<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>3</mn></msup></semantics></math></inline-formula>C-TL-GTO is an empirical quantitative framework for accurate and automatic AD classification, developed and evaluated with the Alzheimer’s Dataset (four classes of images) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The proposed framework reduces the bias and variability of preprocessing steps and hyperparameters optimization to the classifier model and dataset used. Our strategy, evaluated on MRIs, is easily adaptable to other imaging methods. According to our findings, the proposed framework was an excellent instrument for this task, with a significant potential advantage for patient care. The ADNI dataset, an online dataset on Alzheimer’s disease, was used to obtain magnetic resonance imaging (MR) brain images. The experimental results demonstrate that the proposed framework achieves 96.65% accuracy for the Alzheimer’s Dataset and 96.25% accuracy for the ADNI dataset. Moreover, a better performance in terms of accuracy is demonstrated over other state-of-the-art approaches. |
| first_indexed | 2024-03-10T00:50:40Z |
| format | Article |
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| last_indexed | 2024-03-10T00:50:40Z |
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| spelling | doaj.art-216bcbd7bb2e4d5c99d1499f7a4b76f82023-11-23T14:50:59ZengMDPI AGSensors1424-82202022-06-012211425010.3390/s22114250A<sup>3</sup>C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops OptimizerNadiah A. Baghdadi0Amer Malki1Hossam Magdy Balaha2Mahmoud Badawy3Mostafa Elhosseini4College of Nursing, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi ArabiaCollege of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi ArabiaComputers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, EgyptComputers and Control Systems Engineering Department, Faculty of Engineering, Mansoura University, Mansoura 35516, EgyptCollege of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi ArabiaAlzheimer’s disease (AD) is a chronic disease that affects the elderly. There are many different types of dementia, but Alzheimer’s disease is one of the leading causes of death. AD is a chronic brain disorder that leads to problems with language, disorientation, mood swings, bodily functions, memory loss, cognitive decline, mood or personality changes, and ultimately death due to dementia. Unfortunately, no cure has yet been developed for it, and it has no known causes. Clinically, imaging tools can aid in the diagnosis, and deep learning has recently emerged as an important component of these tools. Deep learning requires little or no image preprocessing and can infer an optimal data representation from raw images without prior feature selection. As a result, they produce a more objective and less biased process. The performance of a convolutional neural network (CNN) is primarily affected by the hyperparameters chosen and the dataset used. A deep learning model for classifying Alzheimer’s patients has been developed using transfer learning and optimized by Gorilla Troops for early diagnosis. This study proposes the A<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>3</mn></msup></semantics></math></inline-formula>C-TL-GTO framework for MRI image classification and AD detection. The A<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow></mrow><mn>3</mn></msup></semantics></math></inline-formula>C-TL-GTO is an empirical quantitative framework for accurate and automatic AD classification, developed and evaluated with the Alzheimer’s Dataset (four classes of images) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI). The proposed framework reduces the bias and variability of preprocessing steps and hyperparameters optimization to the classifier model and dataset used. Our strategy, evaluated on MRIs, is easily adaptable to other imaging methods. According to our findings, the proposed framework was an excellent instrument for this task, with a significant potential advantage for patient care. The ADNI dataset, an online dataset on Alzheimer’s disease, was used to obtain magnetic resonance imaging (MR) brain images. The experimental results demonstrate that the proposed framework achieves 96.65% accuracy for the Alzheimer’s Dataset and 96.25% accuracy for the ADNI dataset. Moreover, a better performance in terms of accuracy is demonstrated over other state-of-the-art approaches.https://www.mdpi.com/1424-8220/22/11/4250Alzheimerartificial gorilla troops optimizer (GTO)convolutional neural network (CNN)deep learning (DL)metaheuristic optimization |
| spellingShingle | Nadiah A. Baghdadi Amer Malki Hossam Magdy Balaha Mahmoud Badawy Mostafa Elhosseini A<sup>3</sup>C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer Sensors Alzheimer artificial gorilla troops optimizer (GTO) convolutional neural network (CNN) deep learning (DL) metaheuristic optimization |
| title | A<sup>3</sup>C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer |
| title_full | A<sup>3</sup>C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer |
| title_fullStr | A<sup>3</sup>C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer |
| title_full_unstemmed | A<sup>3</sup>C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer |
| title_short | A<sup>3</sup>C-TL-GTO: Alzheimer Automatic Accurate Classification Using Transfer Learning and Artificial Gorilla Troops Optimizer |
| title_sort | a sup 3 sup c tl gto alzheimer automatic accurate classification using transfer learning and artificial gorilla troops optimizer |
| topic | Alzheimer artificial gorilla troops optimizer (GTO) convolutional neural network (CNN) deep learning (DL) metaheuristic optimization |
| url | https://www.mdpi.com/1424-8220/22/11/4250 |
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