2022
DOI: 10.1080/09540091.2022.2123450
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Deep and hybrid learning of MRI diagnosis for early detection of the progression stages in Alzheimer’s disease

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Cited by 17 publications
(6 citation statements)
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“…Another study related to the results obtained within our model presents some differences in the tools and algorithms used but shares the same objective of identifying patients with Alzheimer's disease [22]. Finally, in research that aimed to implement a convolutional network, no substantial match is found with the goal of our research, as it seeks the categorization of images into groups using certain algorithms to measure the cognitive and functional domains of Alzheimer's patients [23], [24].…”
Section: Discussionmentioning
confidence: 97%
See 1 more Smart Citation
“…Another study related to the results obtained within our model presents some differences in the tools and algorithms used but shares the same objective of identifying patients with Alzheimer's disease [22]. Finally, in research that aimed to implement a convolutional network, no substantial match is found with the goal of our research, as it seeks the categorization of images into groups using certain algorithms to measure the cognitive and functional domains of Alzheimer's patients [23], [24].…”
Section: Discussionmentioning
confidence: 97%
“…The results indicate that the collection of images from an open access series database allows the categorization of images into three groups to extract features from each according to their texture. The combination of these features demonstrates an accuracy of 80% and 60% in the image analysis applied to groups 1 and 3, respectively [23], [24].…”
Section: Literature Reviewmentioning
confidence: 99%
“…5, the confusion matrices for different models offer a detailed look into the performance of the classification models. XGBoost exhibited a robust predictive ability, with only one misclassification out of 32 instances, showcasing its proficiency in accurately distinguishing between cases categorized as healthy (0) and unhealthy (1). The confusion matrices for MLP, RNN, and Random Forest models also displayed competitive outcomes, accurately predicting the majority of instances.…”
Section: Comparative Analysis Of Machine Learning Models For Classifi...mentioning
confidence: 96%
“…Performance metrics, as discussed [1], serve as essential tools for evaluating the effectiveness and precision of diverse models. These models rely on metrics such as accuracy, precision, recall, and the F1 score to make predictions based on provided data.…”
Section: Performance Metricsmentioning
confidence: 99%
“…Performance metrics [37] are utilized to assess the efficacy and precision of various models. Models use the accuracy, precision, recall and f1 score to make predictions based on given data.…”
Section: Performance Metricsmentioning
confidence: 99%