Corrigendum to: Advancing Alzheimer's Disease Diagnosis Using VGG19 and XGBoost: A Neuroimaging-Based Method

<p>The publisher identified that references 42 and 43 were duplicated in the published version of this article [1]. The error has now been corrected. </p> <p> The original article can be found online at: https://www.benthamscience.com/article/150567 </p> <p> Details of the error and the correction are provided below: </p> <p> Original: </p> <p> [42] Muksimova S, Umirzakova S, Iskhakova N, Khaitov A, Cho YI. Advanced convolutional neural network with attention mechanism for Alzheimer’s disease classification using MRI. Comput Biol Med 2025; 190, 110095. </p> <p> [43] Muksimova S, Umirzakova S, Iskhakova N, Khaitov A, Cho YI. Advanced convolutional neural network with attention mechanism for Alzheimer’s disease classification using MRI. Comput Biol Med 2025; 190: 110095. http://dx.doi.org/10.1016/j.compbiomed.2025.110095 PMID: 40158456 </p> <p> Corrected: </p> <p> [42] Muksimova S, Umirzakova S, Iskhakova N, Khaitov A, Cho YI. Advanced convolutional neural network with attention mechanism for Alzheimer’s disease classification using MRI. Comput Biol Med 2025; 190, 110095. http://dx.doi.org/10.1016/j.compbiomed.2025.110095 PMID: 40158456 </p> <p> [43] Olaimat MA, Bozdag S, Saeed F. TA-RNN: An attention-based time-aware recurrent neural network architecture to predict progression of Alzheimer’s disease. Alzheimer’s Dement 2024; 20(S1).</p>