RETRACTED ARTICLE: A voxel based morphometry approach for identifying Alzheimer from MRI images

Saravanakumar, S.; Thangaraj, P.

doi:10.1007/s10586-018-2236-6

Cited by 9 publications

(9 citation statements)

References 22 publications

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“…With a small dataset of 28 people, Zhao and He [ 16 ] combined a deep learning model with SVM using incremental learning for early AD prediction. A voxel-based morphometry (VBM) [ 1 ] approach to ensemble PCA-based bagging and boosting is applied on the MRI dataset. No prior information is required for using VBM even for a small dataset, but the complexity of classification is higher in ensemble classifications.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In the modern competitive world, the lifestyle of the person is hustled, irregular, and stretched towards multitasking, resulting in many health-related issues leading to long-term chronic diseases. Alzheimer's disease (AD) belongs to the category of diseases associated with progressive dementia that causes impairment of memory, thought process, and conduct [ 1 ]. Symptoms typically grow gradually and deteriorate over the time, eventually making it impossible for the patient to perform even basic everyday tasks, as the patient gradually loses ability to control their body, and in cases, can lead to death [ 2 ].…”

Section: Introductionmentioning

confidence: 99%

“…Medical resonance imaging (MRI) is a non-invasive technique, which is comparatively cheaper than invasive methods, and is easier to understand from an analytical point of view as it captures brain microstructure with a high spatial resolution. Hence, MRI has been used to generate data regarding informative biomarkers for mapping Alzheimer's disease [ 1 , 2 ]. Recent advancements in data acquisition and analysis techniques have made it possible to analyze the volumes of medical data.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Performances of Machine Learning Models for Diagnosis of Alzheimer’s Disease

Arjaria¹,

Rathore²,

Bisen

et al. 2022

Ann. Data. Sci.

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In recent times, various machine learning approaches have been widely employed for effective diagnosis and prediction of diseases like cancer, thyroid, Covid-19, etc. Likewise, Alzheimer’s (AD) is also one progressive malady that destroys memory and cognitive function over time. Unfortunately, there are no dedicated AI-based solutions for diagnoses of AD to go hand in hand with medical diagnosis, even though multiple factors contribute to the diagnosis, making AI a very viable supplementary diagnostic solution. This paper reports an endeavor to apply various machine learning algorithms like SGD, k-Nearest Neighbors, Logistic Regression, Decision tree, Random Forest, AdaBoost, Neural Network, SVM, and Naïve Bayes on the dataset of affected victims to diagnose Alzheimer’s disease. Longitudinal collections of subjects from OASIS dataset have been used for prediction. Moreover, some feature selection and dimension reduction methods like Information Gain, Information Gain Ratio, Gini index, Chi-Squared, and PCA are applied to rank different factors and identify the optimum number of factors from the dataset for disease diagnosis. Furthermore, performance is evaluated of each classifier in terms of ROC-AUC, accuracy, F1 score, recall, and precision as well as included comparative analysis between algorithms. Our study suggests that approximately 90% classification accuracy is observed under top-rated four features CDR, SES, nWBV, and EDUC.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Performances of Machine Learning Models for Diagnosis of Alzheimer’s Disease

Arjaria¹,

Rathore²,

Bisen

et al. 2022

Ann. Data. Sci.

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“…A diferencia de otros estudios, este utilizó MRI estructural basándose en la técnica de morfometría basada en vóxels (VBM). A juicio de varios estudios 29,30 , la VBM otorga una mayor prolijidad en el diagnóstico al dividir al cerebro en múltiples regiones de algunos milímetros de volumen llamados vóxel y por ser una técnica automatizada que coopera con el observador humano. En la misma senda, la inteligencia artificial busca apoyar al experto a lograr diagnósticos con mayor objetividad.…”

Section: Biomarcadores De Imágenesunclassified

“…Considerando la importancia de lograr biomarcadores para el diagnóstico precoz de la EA, y en vista de los resultados mostrados por estudios con MRI y PET, lo más eficaz es la integración de las distintas técnicas 30,40 . Así, un reciente estudio desarrolló un algoritmo para ML de aprendizaje profundo (deep learning) para detectar individuos en riesgo de EA; dicho algoritmo automatizado se basa en datos obtenidos por PET y MRI, determinando que el diagnóstico temprano es más acucioso utilizando ambas técnicas en vez de cada una por separado 41,42 .…”

Section: Biomarcadores De Imágenesunclassified

Biomarcadores de neuroimagen para el diagnóstico temprano de la enfermedad de Alzheimer. Un abordaje desde las redes neuronales

Torres

2020

Rev. chil. radiol.

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Neuroimaging biomarkers for early diagnosis of Alzheimer's Disease. An approach from neural networks Resumen:Las crecientes cifras mundiales de prevalencia e incidencia de la Enfermedad de Alzheimer exigen el desarrollo de métodos diagnósticos cada vez más precoces. La enfermedad suele diagnosticarse cuando la patología ya está avanzada y poco se puede hacer terapéuticamente, conllevando una gran pérdida de años de vida y altos costos sociales y familiares. Considerando esta patología como una disrupción a gran escala de las redes neuronales del cerebro humano, distintos estudios han propuesto biomarcadores basados en resonancia magnética y tomografía por emisión de positrones. El presente artículo revisa sistemáticamente dichos estudios considerando un abordaje desde la ciencia de redes neuronales.

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