Corrigendum to: A Comprehensive Review on Deep Learning Techniques in Alzheimer's Disease Diagnosis

In the originally published article entitled "A Comprehensive Review on Deep Learning Techniques in Alzheimer's Disease Diagnosis", published in "Current Topics in Medicinal Chemistry" Vol: 25 Issue: 04 [1], certain phrases and expressions were unclear, which may have affected readability. These have now been revised to improve clarity and ensure that the intended meaning is accurately conveyed. The corrections do not affect the results, interpretations, or conclusions of the article. The original article can be found online at https://www.eurekaselect.com/article/140894 Details of the error and a correction are provided here: ORIGINAL Moreover, DBN is a graphical model that investigators use to obtain a deep hierarchical representation of training data and is frequently used for AD detection. An unsupervised probabilistic Deep Learning Technique is created by pre-training DBN models using the greedy learning approach. From bottom to top, layers of RBMs are stacked to create the DBN architecture. Each RBM layer includes both a visible and concealed layer. The top two levels of the DBN structure have an undirected or symmetric link, whereas the lowest layers have a direct connection. DBN building is comparable to RBM construction in that the first RBM is made through training, after which the weights are fixed, and the concealed layer is established as the RBM's next visible layer. The next RBMs go through this procedure iteratively [79]. CORRECTED Moreover, DBN is a graphical model that investigators use to obtain a deep hierarchical representation of training data and is frequently used for AD detection. An unsupervised probabilistic Deep Learning Technique is created by pre-training DBN models using the greedy learning approach. From bottom to top, layers of RBMs are stacked to create the DBN architecture. Each RBM layer includes both a visible and hidden layer. The top two levels of the DBN structure have an undirected or symmetric link, whereas the lowest layers have a direct connection. DBN building is comparable to RBM construction in that the first RBM is made through training, after which the weights are fixed, and the hidden layer is established as the RBM's next visible layer. The next RBMs go through this procedure iteratively [79]. The authors apologize for any inconvenience caused.