Journal of neuroscience methods · 2017
Deep learning reveals Alzheimer's disease onset in MCI subjects: Results from an international challenge.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Amoroso Nicola, Diacono Domenico, Fanizzi Annarita, La Rocca Marianna, Monaco Alfonso, Lombardi Angela, Guaragnella Cataldo, Bellotti Roberto, Tangaro Sabina, Alzheimer's Disease Neuroimaging Initiative
Аннотация
BACKGROUND: Early diagnosis of Alzheimer's disease (AD) and its onset in subjects affected by mild cognitive impairment (MCI) based on structural MRI features is one of the most important open issues in neuroimaging. Accordingly, a scientific challenge has been promoted, on the international Kaggle platform, to assess the performance of different classification methods for prediction of MCI and its conversion to AD.
NEW METHOD: This work presents a classification strategy based on Random Forest feature selection and Deep Neural Network classification using a mixed cohort including the four classes of classification problem, that is HC, AD, MCI and cMCI, to train the model. Moreover, we compare this approach with a novel classification strategy based on fuzzy logic learned on a mixed cohort including only HC and AD.
EXPERIMENTS: A training set of 240 subjects and a test set including mixed cohort of 500 real and simulated subjects were used. The data included AD patients, MCI subjects converting to AD (cMCI), MCI subjects and healthy controls (HC). This work ranked third for overall accuracy (38.8%) over 19 participating teams.
COMPARISON WITH EXISTING METHOD(S): The "International challenge for automated prediction of MCI from MRI data" hosted by the Kaggle platform has been promoted to validate different methodologies with a common set of data and evaluation procedures.
CONCLUSION: DNNs reach a classification accuracy significantly higher than other machine learning strategies; on the other hand, fuzzy logic is particularly accurate with cMCI, suggesting a combination of these approaches could lead to interesting future perspectives.
NEW METHOD: This work presents a classification strategy based on Random Forest feature selection and Deep Neural Network classification using a mixed cohort including the four classes of classification problem, that is HC, AD, MCI and cMCI, to train the model. Moreover, we compare this approach with a novel classification strategy based on fuzzy logic learned on a mixed cohort including only HC and AD.
EXPERIMENTS: A training set of 240 subjects and a test set including mixed cohort of 500 real and simulated subjects were used. The data included AD patients, MCI subjects converting to AD (cMCI), MCI subjects and healthy controls (HC). This work ranked third for overall accuracy (38.8%) over 19 participating teams.
COMPARISON WITH EXISTING METHOD(S): The "International challenge for automated prediction of MCI from MRI data" hosted by the Kaggle platform has been promoted to validate different methodologies with a common set of data and evaluation procedures.
CONCLUSION: DNNs reach a classification accuracy significantly higher than other machine learning strategies; on the other hand, fuzzy logic is particularly accurate with cMCI, suggesting a combination of these approaches could lead to interesting future perspectives.