Abstract
Background: Alzheimer’s is a degenerative dementing disorder that starts with a mild memory impairment and progresses to a total loss of mental and physical faculties. The sooner the diagnosis is made, the better for the patient, as preventive actions and treatment can be started. Although tests such as the Mini-Mental State Tests Examination are usually used for early identification, diagnosis relies on magnetic resonance imaging (MRI) brain analysis. Methods: Public initiatives such as the OASIS (Open Access Series of Imaging Studies) collection provide neuroimaging datasets openly available for research purposes. In this work, a new method based on deep learning and image processing techniques for MRI-based Alzheimer’s diagnosis is proposed and compared with previous literature works. Results: Our method achieves a balance accuracy (BAC) up to 0.93 for image-based automated diagnosis of the disease, and a BAC of 0.88 for the establishment of the disease stage (healthy tissue, very mild and severe stage). Conclusions: Results obtained surpassed the state-of-the-art proposals using the OASIS collection. This demonstrates that deep learning-based strategies are an effective tool for building a robust solution for Alzheimer’s-assisted diagnosis based on MRI data.
Highlights
Back in 1907, Alois Alzheimer described how a 51-year-old woman died with severe dementia after four years of rapid memory degeneration
The initial clinical manifestations of Alzheimer’s disease (AD) are difficult to define as there is a large variation between cognitive abnormalities, but they can be correlated to degeneration in some specific regions of the brain
‘Educ’ indicates the years of education, Socioeconomic status (SES) uses the Hollinshead index of Social Position [32], Mini-Mental State Examination (MMSE) score [2] and Clinical Dementia Rating (CDR) scale [4] were established after medical examination, estimated total intracranial volume (eTIV) [33] and normalized whole-brain volume (nWBV) [34] were calculated as standard methods to analyze anatomical characteristics of the brain in the magnetic resonance imaging (MRI) images, atlas scaling factor (ASF) [33] was computed to transform the brain and skull from native space to the selected target atlas and ‘Delay’
Summary
Back in 1907, Alois Alzheimer described how a 51-year-old woman died with severe dementia after four years of rapid memory degeneration. Alzheimer’s disease, named after him, is a dementia degenerative disease starting with mild memory impairment in the early stages and progressing to a complete loss of the mental and physical faculties [1]. Alzheimer’s is a degenerative dementing disorder that starts with a mild memory impairment and progresses to a total loss of mental and physical faculties. The sooner the diagnosis is made, the better for the patient, as preventive actions and treatment can be started Tests such as the Mini-Mental State Tests Examination are usually used for early identification, diagnosis relies on magnetic resonance imaging (MRI) brain analysis. Conclusions: Results obtained surpassed the state-of-the-art proposals using the OASIS collection This demonstrates that deep learning-based strategies are an effective tool for building a robust solution for Alzheimer’s-assisted diagnosis based on MRI data
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