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Automated classification of Alzheimer's disease, mild cognitive impairment, and cognitively normal patients using 3D convolutional neural network and radiomic features from T1-weighted brain MRI: A comparative study on detection accuracy
Research article (Clinical Imaging, 2024) · cited 14× · AI/ML
Automated classification of Alzheimer's disease, mild cognitive impairment, and cognitively normal patients using 3D convolutional neural network and radiomic features from T1-weighted brain MRI: A comparative study on detection accuracy
Summary
Automated classification of Alzheimer's disease, mild cognitive impairment, and cognitively normal patients using 3D convolutional neural network and radiomic features from T1-weighted brain MRI: A comparative study on detection accuracy is a scholarly article[1].
Key Facts
Automated classification of Alzheimer's disease, mild cognitive impairment, and cognitively normal patients using 3D convolutional neural network and radiomic features from T1-weighted brain MRI: A comparative study on detection accuracy's instance of is recorded as scholarly article[2].
References
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Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Automated classification of Alzheimer's disease, mild cognitive impairment, and cognitively normal patients using 3D convolutional neural network and radiomic features from T1-weighted brain MRI: A comparative study on detection accuracy. Retrieved May 24, 2026, from https://4ort.xyz/entity/automated-classification-of-alzheimer-s-disease-mild-cognitive-impairment-and-cognitively-normal-patients-using-3d-convo
MLA“Automated classification of Alzheimer's disease, mild cognitive impairment, and cognitively normal patients using 3D convolutional neural network and radiomic features from T1-weighted brain MRI: A comparative study on detection accuracy.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/automated-classification-of-alzheimer-s-disease-mild-cognitive-impairment-and-cognitively-normal-patients-using-3d-convo.
BibTeX@misc{4ortxyz_automated-classification-of-alzheimer-s-disease-mild-cognitive-impairment-and-cognitively-normal-patients-using-3d-convo_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Automated classification of Alzheimer's disease, mild cognitive impairment, and cognitively normal patients using 3D convolutional neural network and radiomic features from T1-weighted brain MRI: A comparative study on detection accuracy}}, year = {2026}, url = {https://4ort.xyz/entity/automated-classification-of-alzheimer-s-disease-mild-cognitive-impairment-and-cognitively-normal-patients-using-3d-convo}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Automated classification of Alzheimer's disease, mild cognitive impairment, and cognitively normal patients using 3D convolutional neural network and radiomic features from T1-weighted brain MRI: A comparative study on detection accuracy — https://4ort.xyz/entity/automated-classification-of-alzheimer-s-disease-mild-cognitive-impairment-and-cognitively-normal-patients-using-3d-convo (retrieved 2026-05-24)