Artificial Intelligence in Medicine Using Quantum Computing in the Future of Healthcare

Davids, Joseph; Lidströmer, Niklas; Ashrafian, Hutan

doi:10.1007/978-3-030-58080-3_338-1

Cited by 1 publication

(1 citation statement)

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“…CNNs comprise several important components, including the convolutional layer, activation function, pooling layer, and fully connected layer. Each of these layers performs a specific function in the network architecture [15].…”

Section: B Time Rate Of Acceleration Of Alzheimer's Diseasementioning

confidence: 99%

Staging of Alzheimer's disease based on MRI using CNN

Baddour,

Ali

2023

ESS

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Early detection of chronic diseases and determining the stages of damage to the patient is considered one of the most important stages of treatment, as it helps doctors take important remedial measures that help the patient recover or reduce the risk of the disease to a minimum. Alzheimer's disease is one of the neurological diseases that lead to brain atrophy, which leads to the loss of its functions. MRI images of the brain are used to detect Alzheimer's disease, but it is difficult to determine both the stages of the disease and the amount of damage in a patient using this MRI technique. In this research, we aim to detect Alzheimer's disease in addition to determining its stage based on deep learning techniques by using a classifier that uses the convolutional neural network (CNN). In the research, magnetic resonance images of the brain were used, and the hippocampus region was extracted in assessing the amount of damage because it is the most important region in diagnosing damage to the disease and reduce the amount of data entered into the neural network, our results show an accuracy of 95% in estimating brain damage. The results of the classifier used were able to determine the amount of damage according to four stages of the disease.

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Section: B Time Rate Of Acceleration Of Alzheimer's Diseasementioning

confidence: 99%