Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
A Review of Deep Transfer Learning Approaches for Class-Wise Prediction of Alzheimer’s Disease Using MRI Images
https://orcid.org/0000-0001-5140-5743
Abstract Alzheimer's disease is an irreversible, progressive neurodegenerative disorder that destroys the brain and memory functionalities. In Alzheimer's disease, the brain starts shrinking, and over time it converts into dementia. The diagnosis of dementia takes an ample amount of time, around 2.8 to 4.4 years after the first clinical symptoms arise. Alzheimer's disease cannot be cured by any pharmacologic therapies (drugs) now on the market. Alzheimer's disease can only be avoided by early detection and prompt treatment. This paper proposes deep transfer learning models and MRI (Magnetic Resonance Imaging) images to detect the multiple stages of Alzheimer's disease such as "Very-Mild -Demented," "Mild-Demented," "Moderate-Demented," and "No-Demented." Data preprocessing and augmentation process are applied, enabling the model to detect the correct class of Alzheimer's disease. Then further deep transfer learning models (Resnet50, VGG19, Xception, DenseNet201, and EfficientNetB7) are used to classify and predict the early stages of Alzheimer's disease. It is observed that the DenseNet201 model performs the best, with a validation accuracy of 96.59%. The performance of Resnet50, VGG19, Xception, and EfficientNetB7 models was also recorded with validation accuracy of 93.52%, 95.08%, 89.77%, and 83.20%, respectively. The probability curve is then measured and the class-wise prediction of Alzheimer's disease is recorded using the area under curves and receiver operating curve (AUC-ROC) in order to analyze it more deeply.
A Review of Deep Transfer Learning Approaches for Class-Wise Prediction of Alzheimer’s Disease Using MRI Images
https://orcid.org/0000-0001-5140-5743
Abstract Alzheimer's disease is an irreversible, progressive neurodegenerative disorder that destroys the brain and memory functionalities. In Alzheimer's disease, the brain starts shrinking, and over time it converts into dementia. The diagnosis of dementia takes an ample amount of time, around 2.8 to 4.4 years after the first clinical symptoms arise. Alzheimer's disease cannot be cured by any pharmacologic therapies (drugs) now on the market. Alzheimer's disease can only be avoided by early detection and prompt treatment. This paper proposes deep transfer learning models and MRI (Magnetic Resonance Imaging) images to detect the multiple stages of Alzheimer's disease such as "Very-Mild -Demented," "Mild-Demented," "Moderate-Demented," and "No-Demented." Data preprocessing and augmentation process are applied, enabling the model to detect the correct class of Alzheimer's disease. Then further deep transfer learning models (Resnet50, VGG19, Xception, DenseNet201, and EfficientNetB7) are used to classify and predict the early stages of Alzheimer's disease. It is observed that the DenseNet201 model performs the best, with a validation accuracy of 96.59%. The performance of Resnet50, VGG19, Xception, and EfficientNetB7 models was also recorded with validation accuracy of 93.52%, 95.08%, 89.77%, and 83.20%, respectively. The probability curve is then measured and the class-wise prediction of Alzheimer's disease is recorded using the area under curves and receiver operating curve (AUC-ROC) in order to analyze it more deeply.
A Review of Deep Transfer Learning Approaches for Class-Wise Prediction of Alzheimer’s Disease Using MRI Images
https://orcid.org/0000-0001-5140-5743
Abstract Alzheimer's disease is an irreversible, progressive neurodegenerative disorder that destroys the brain and memory functionalities. In Alzheimer's disease, the brain starts shrinking, and over time it converts into dementia. The diagnosis of dementia takes an ample amount of time, around 2.8 to 4.4 years after the first clinical symptoms arise. Alzheimer's disease cannot be cured by any pharmacologic therapies (drugs) now on the market. Alzheimer's disease can only be avoided by early detection and prompt treatment. This paper proposes deep transfer learning models and MRI (Magnetic Resonance Imaging) images to detect the multiple stages of Alzheimer's disease such as "Very-Mild -Demented," "Mild-Demented," "Moderate-Demented," and "No-Demented." Data preprocessing and augmentation process are applied, enabling the model to detect the correct class of Alzheimer's disease. Then further deep transfer learning models (Resnet50, VGG19, Xception, DenseNet201, and EfficientNetB7) are used to classify and predict the early stages of Alzheimer's disease. It is observed that the DenseNet201 model performs the best, with a validation accuracy of 96.59%. The performance of Resnet50, VGG19, Xception, and EfficientNetB7 models was also recorded with validation accuracy of 93.52%, 95.08%, 89.77%, and 83.20%, respectively. The probability curve is then measured and the class-wise prediction of Alzheimer's disease is recorded using the area under curves and receiver operating curve (AUC-ROC) in order to analyze it more deeply.
A Review of Deep Transfer Learning Approaches for Class-Wise Prediction of Alzheimer’s Disease Using MRI Images
Sisodia, Pushpendra Singh (Autor:in) / Ameta, Gaurav Kumar (Autor:in) / Kumar, Yogesh (Autor:in) / Chaplot, Neelam (Autor:in)
2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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