Convolutional Neural Networks for Alzheimer’s Detection Using Landmark-Based Hippocampus Slices
Keywords:
Convolutional neural network, multiclass classification, axial view, coronal view, sagittal view”.Abstract
This study tackles the urgent requirement for enhanced diagnostic precision in Alzheimer's disease (ad) by using devising a modern MRI-based approach concentrating at the hippocampal region. Traditional gadget gaining knowledge of algorithms have confronted challenges with irrelevant data from whole MRI photographs, impeding specific Alzheimer's disease categorization. The study utilizes Convolutional Neural Networks (CNNs), mainly Pretrained ResNet50, ResNet50, and LeNet, to awareness on unique slices of the hippocampus to improve diagnostic accuracy. CNN models are trained on manually extracted hippocampus areas the usage of the Alzheimer's disease Neuroimaging Initiative (ADNI) dataset. by means of focusing on this essential area related to advert pathophysiology, the fashions are delicate to appropriately stumble on suggestive patterns. The have a look at additionally examines the effectiveness of LeNet with Dropout, illustrating its capacity to attain 100% accuracy. This studies highlights the importance of focused on unique brain areas in Alzheimer's disease diagnosis, providing a possible road for better detection techniques. The outcomes no longer simplest decorate comprehension of Alzheimer's disease pathology however also provide practical implications for enhancing medical diagnostic instruments, in the long run helping in more powerful control and intervention strategies for this devastating situation.
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