2015-2022. This data set contains data from BRFSS.

Use this dataset for classifying if there is dementia forming up in the brain or not. MCI refers to mild cognitive impairment(early stage of Alzheimer) and CN refers to Cognitive Impairment(No dementia)

Alzheimer_MRI Disease Classification Dataset

The Falah/Alzheimer_MRI Disease Classification dataset is a valuable resource for researchers and health medicine applications. This dataset focuses on the classification of Alzheimer's disease based on MRI scans. The dataset consists of brain MRI images labeled into four categories:

'0': Mild_Demented '1': Moderate_Demented '2': Non_Demented '3': Very_Mild_Demented

  Dataset Information

Train split:

Name: train Number of… See the full description on the dataset page: https://huggingface.co/datasets/Falah/Alzheimer_MRI.

Alzheimer's Disease Detection

## Overview

Alzheimer's Disease Detection is a dataset for object detection tasks - it contains ND annotations for 6,400 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The National Institute on Aging Genetics of Alzheimer's Disease Data Storage Site (NIAGADS) is a national genetics data repository facilitating access to genotypic and phenotypic data for Alzheimer's disease (AD). Data include GWAS, whole genome (WGS) and whole exome (WES), expression, RNA Seq, and CHIP Seq analyses. Data for the Alzheimer’s Disease Sequencing Project (ADSP) are available through a partnership with dbGaP (ADSP at dbGaP). Results are integrated and annotated in the searchable genomics database that also provides access to a variety of software packages, analytic pipelines, online resources, and web-based tools to facilitate analysis and interpretation of large-scale genomic data. Data are available as defined by the NIA Genomics of Alzheimer’s Disease Sharing Policy and the NIH Genomics Data Sharing Policy. Investigators return secondary analysis data to the database in keeping with the NIAGADS Data Distribution Agreement.

No description was included in this Dataset collected from the OSF

In the United States, around 39 percent of people with Alzheimer’s are 75 to 84 years old. Additionally, around 26 percent of those with Alzheimer’s are aged 65 to 74 years. Alzheimer’s disease is a form of dementia which impacts memory, behavior, and thinking and can lead to symptoms becoming so severe that those with the disease require support with basic daily tasks. Alzheimer’s remains a relevant problem around the world. Alzheimer’s disease deaths Alzheimer’s is currently the sixth leading cause of death in the United States, causing more deaths than diabetes and kidney disease. While advances in medicine and increased access to treatment and care have caused decreases in many major causes of death, deaths from Alzheimer’s have risen over the past couple of decades. For example, from 2000 to 2022, deaths from stroke in the U.S. declined by 1.4 percent, while deaths from Alzheimer’s increased 142 percent. Alzheimer’s disease worldwide Alzheimer’s is not only a problem in the United States but impacts every country around the globe. In 2018, there were an estimated 50 million people living with dementia worldwide. This figure is predicted to increase to some 152 million by the year 2050. Alzheimer’s does not only cause a significant amount of death but also has a significant economic impact. In 2018, cost estimates for Alzheimer’s care worldwide totaled around one trillion U.S. dollars, with this figure predicted to double by the year 2030.

EWA-DB is a speech database that contains data from 3 clinical groups: Alzheimer's disease, Parkinson's disease, mild cognitive impairment, and a control group of healthy subjects. Speech samples of each clinical group were obtained using the EWA smartphone application, which contains 4 different language tasks: sustained vowel phonation, diadochokinesis, object and action naming (30 objects and 30 actions), picture description (two single pictures and three complex pictures).The total number of speakers in the database is 1649. Of these, there are 87 people with Alzheimer's disease, 175 people with Parkinson's disease, 62 people with mild cognitive impairment, 2 people with a mixed diagnosis of Alzheimer's + Parkinson's disease and 1323 healthy controls.For speakers who provided written consent (total number of 1003 speakers), we publish audio recordings in WAV format. We are also attaching a JSON file with ASR transcription, if available manual annotation (available for 965 speakers) and additional information about the speaker. For speakers who did not give their consent to publish the recording, only the JSON file is provided. ASR transcription is provided for all 1649 speakers. All 1649 speakers gave their consent to the provider to process their audio recordings. Therefore, it is possible for third party researchers to carry out their experiments also on the unpublished audio recordings through cooperation with the provider.

SilpaCS/Alzheimer dataset hosted on Hugging Face and contributed by the HF Datasets community

Global Alzheimer's therapeutics market is estimated to be USD 4,288.8 million in 2025 and is also expected to grow to reach USD 10,433.9 million by 2035. In addition to this, it is also projected to grow at a CAGR of 9.3% during the forecast period of 2025 to 2035. In the year 2024, it has generated revenue of USD 3,873.1 million from Alzheimer's therapeutics.

Metric	Value
Industry Size (2025E)	USD 4,288.8 million
Industry Value (2035F)	USD 10,433.9 million
CAGR (2025 to 2035)	9.3%

Semi Annual Market Update

Particular	Value CAGR
H1	9.2% (2024 to 2034)
H2	9.7% (2024 to 2034)
H1	9.3% (2025 to 2035)
H2	9.9% (2025 to 2035)

Country-wise Insights

Countries	Value CAGR (2025 to 2035)
United States	9.0%
Germany	11.0%
UK	9.7%
Japan	12.0%
India	11.6%
South Korea	9.5%
Australia & New Zealand	3.0%
South Korea	9.3%

Category-wise Insights

By Drug Name	Value Share (2025)
Donepezil	67.7%

By Distribution Channels	Value Share (2025)
Hospital Pharmacies	35.0%

Deaths registered in 2019 in England and Wales due to dementia and Alzheimer's disease, by sex, age group, ethnicity, region and place of occurrence. Includes analysis of comorbidities.

Alzheimer

## Overview

Alzheimer is a dataset for classification tasks - it contains Alzheimer Ufd1 annotations for 500 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) began in 2004 as longitudinal multicenter study to identify clinical, imaging, genetic, and biochemical biomarkers for the detection and tracking of Alzheimer’s disease (AD). Participants are recruited from and followed at 59 research sites in the United States and Canada.

• ADNI1 enrolled 400 subjects who were diagnosed with mild cognitive impairment (MCI), 200 subjects with early AD, and 200 elderly control subjects. Data collected included MRI and PET brain scans, genetic profiles, and blood and cerebrospinal fluid biomarkers.
• ADNI GO began in 2009 and included all ADNI1 participants and enrolled 200 new participants with early mild cognitive impairment (EMCI).
• ADNI2 began in 2011 and recruited an additional 150 elderly control subjects, 100 ECMI subjects, 150 late mild cognitive impairment (LMCI) subjects, and 150 subjects with mild AD. ADNI2 also added a new cohort group, Significant Memory Concern (SMC), and enrolled 107 subjects with self-reported significant memory concerns. During this phase, collection of amyloid PET with Florbetapir was added to study protocols at all ADNI2 sites for all ADNI2 and ADNI GO subjects.
• ADNI3 began in 2016 with an anticipated enrollment of 150-515 new MCI subjects, 85-185 mild AD subjects, and 135-500 elderly control subjects. ADNI3 data includes longitudinal tau PET scans with AV 1451 and brain scans that detect tau protein tangles (tau PET).

The Roche Alzheimer’s disease dataset (Roche_AD) consists of 80 samples from 40 unique individuals (one sample from the temporal cortex and one from deep white matter for each individual, 12 cases, 25 controls, 3 dementia). A total of 12,000 estimated cells from each sample were loaded on the 10x Single Cell Next GEM G Chip. cDNA libraries were prepared using the Chromium Single Cell 3’ Library and Gel Bead v3 kit according to the manufacturer’s instructions. cDNA libraries were sequenced using the Illumina NovaSeq 6000 System and NovaSeq 6000 S2 Reagent Kit v1.5 (100 cycles), aiming at a sequencing depth of minimum 30K reads/nucleus.

As of January 1, 2025, 35 percent of Alzheimer’s disease drugs in clinical trial phase III of development worldwide addressed neurotransmitter receptor mechanisms. This statistic illustrates Alzheimer’s disease drugs in phase III of development worldwide as of early 2025, by type.

The Alzheimer's Disease Diagnostics and Therapeutics Market Report is Segmented by Product (Therapeutics and Diagnostics) and Geography (North America, Europe, Asia-Pacific, Middle East, Africa, and South America). The Report Offers the Market Sizes and Forecasts in Value (USD) for all the Above Segments.

Organized by zipcode: Rates of Alzheimer's disease Percent of landcover types Modelled PM2.5 Socioeconomic variables. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Lucas Neas (CPHEA/PHESD/EB) is the owner of the copy of this dataset that was used. Format: Medicare database. This dataset is associated with the following publication: Wu, J., and L. Jackson. Greenspace inversely associated with the risk of Alzheimer’s disease in the mid-Atlantic United States. Earth. MDPI AG, Basel, SWITZERLAND, 2(1): 140-150, (2021).

Dataset from the paper "EEG functional connectivity and deep learning for automatic diagnosis of brain disorders: Alzheimer's disease and schizophrenia".Schizophrenia dataset also available in: http://brain.bio.msu.ru/eeg_schizophrenia.htm

This dataset has data files from 5-Choice, Pairwise Visual Discrimination (PVD), and Paired-Associates Learning (PAL) cognitive behavioral tasks in which male and female mice from 3 Alzheimer’s mouse models were tested using touchscreen technology at two different sites (e.g. University of Western Ontario and University of Guelph). Both aggregated data and trial-by-trial data are provided in this dataset.

The clinical, radiomics and genetic data to reproduce the key findings in "A predictive model using the mesoscopic architecture of the living brain to detect Alzheimer’s disease".

Alzheimer’s disease is the most common cause of dementia. It is a neurodegenerative disorder characterized by gradually progressive cognitive and functional deficits, as well as behavioral changes. The diagnosis of Alzheimer’s disease is often challenging leading to suboptimal patient care. In this study, we develop a new unsupervised analytic method based on the extraction of statistical features from multiple brain regions identified through structural magnetic resonance imaging data, which is able to reliably discriminate people with Alzheimer’s disease-related pathologies from those without. We provide a diagnostic tool that is ready to be integrated into the clinical decision support system without the need for additional sampling or patient testing.