{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T14:00:14Z","timestamp":1788184814996,"version":"build-2803163510"},"reference-count":44,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T00:00:00Z","timestamp":1733184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:p>One of the most prevalent disorders relating to neurodegenerative conditions and dementia is Alzheimer's disease (AD). In the age group 65 and older, the prevalence of Alzheimer's disease is increasing. Before symptoms showed up, the disease had grown to a severe stage and resulted in an irreversible brain disorder that is not treatable with medication or other therapies. Therefore, early prediction is essential to slow down AD progression. Computer-aided diagnosis systems can be used as a second opinion by radiologists in their clinics to predict AD using MRI scans. In this work, we proposed a novel deep learning architecture named DenseIncepS115for for AD prediction from MRI scans. The proposed architecture is based on the Inception Module with Self-Attention (InceptionSA) and the Dense Module with Self-Attention (DenseSA). Both modules are fused at the network level using a depth concatenation layer. The proposed architecture hyperparameters are initialized using Bayesian Optimization, which impacts the better learning of the selected datasets. In the testing phase, features are extracted from the depth concatenation layer, which is further optimized using the Catch Fish Optimization (CFO) algorithm and passed to shallow wide neural network classifiers for the final prediction. In addition, the proposed DenseIncepS115 architecture is interpreted through Lime and Gradcam explainable techniques. Two publicly available datasets were employed in the experimental process: Alzheimer's ADNI and Alzheimer's classes MRI. On both datasets, the proposed architecture obtained an accuracy level of 99.5% and 98.5%, respectively. Detailed ablation studies and comparisons with state-of-the-art techniques show that the proposed architecture outperforms.<\/jats:p>","DOI":"10.3389\/fonc.2024.1501742","type":"journal-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T05:10:37Z","timestamp":1733202637000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["DenseIncepS115: a novel network-level fusion framework for Alzheimer's disease prediction using MRI images"],"prefix":"10.3389","volume":"14","author":[{"given":"Fatima","family":"Rauf","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Attique","family":"Khan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghassen Ben","family":"Brahim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wardah","family":"Abrar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Areej","family":"Alasiry","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehrez","family":"Marzougui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seob","family":"Jeon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunyoung","family":"Nam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1007\/s12035-023-03578-3","article-title":"Non-invasive biomarkers for early detection of Alzheimer\u2019s disease: a new-age perspective","volume":"61","author":"Chimthanawala","year":"2024","journal-title":"Mol Neurobiol"},{"key":"B2","volume-title":"World Alzheimer report 2022: life after diagnosis: navigating treatment, care and support","author":"Gauthier","year":"2023"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12559-023-10192-x","article-title":"Explainable artificial intelligence in Alzheimer\u2019s disease classification: A systematic review","volume":"16","author":"Viswan","year":"2024","journal-title":"Cogn Comput"},{"key":"B4","doi-asserted-by":"publisher","first-page":"714","DOI":"10.3390\/bioengineering10060714","article-title":"AHANet: Adaptive hybrid attention network for Alzheimer\u2019s disease classification using brain magnetic resonance imaging","volume":"10","author":"Illakiya","year":"2023","journal-title":"Bioengineering"},{"key":"B5","doi-asserted-by":"publisher","first-page":"100223","DOI":"10.1016\/j.health.2023.100223","article-title":"An alzheimer\u2019s disease classification method using fusion of features from brain magnetic resonance image transforms and deep convolutional networks","volume":"4","author":"Asgharzadeh-Bonab","year":"2023","journal-title":"Healthcare Analytics"},{"key":"B6","doi-asserted-by":"publisher","first-page":"102332","DOI":"10.1016\/j.artmed.2022.102332","article-title":"Deep learning for Alzheimer's disease diagnosis: A survey","volume":"130","author":"Khojaste-Sarakhsi","year":"2022","journal-title":"Artif Intell Med"},{"key":"B7","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2022.3197331","article-title":"An attention-based 3D CNN with multi-scale integration block for Alzheimer's disease classification","volume":"26","author":"Wu","year":"2022","journal-title":"IEEE J Biomed Health Inf"},{"key":"B8","doi-asserted-by":"publisher","first-page":"17037","DOI":"10.1038\/s41598-024-66874-5","article-title":"Exceptional performance with minimal data using a generative adversarial network for alzheimer's disease classification","volume":"14","author":"Wong","year":"2024","journal-title":"Sci Rep"},{"key":"B9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1049\/ccs2.12072","article-title":"Review and analysis of deep neural network models for Alzheimer's disease classification using brain medical resonance imaging","volume":"5","author":"Pallawi","year":"2023","journal-title":"Cogn Comput Syst"},{"key":"B10","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1002\/wps.21159","article-title":"Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions","volume":"23","author":"Voineskos","year":"2024","journal-title":"World Psychiatry"},{"key":"B11","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.cger.2022.07.004","article-title":"Brain networks, clinical manifestations, and neuroimaging of cognitive disorders: The role of computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography (PET), and other advanced neuroimaging tests","volume":"39","author":"Bonakdarpour","year":"2023","journal-title":"Clinics Geriatric Med"},{"key":"B12","doi-asserted-by":"publisher","DOI":"10.3389\/fpubh.2022.834032","article-title":"Evaluation of neuro images for the diagnosis of Alzheimer's disease using deep learning neural network","volume":"10","author":"Hamdi","year":"2022","journal-title":"Front Public Health"},{"key":"B13","first-page":"100749","article-title":"Prediction and classification of Alzheimer disease categories using integrated deep transfer learning approach","volume":"27","author":"Leela","year":"2023","journal-title":"Measurement: Sensors"},{"key":"B14","doi-asserted-by":"publisher","first-page":"451","DOI":"10.11591\/ijai.v12.i1.pp451-458","article-title":"A novel automated deep learning approach for Alzheimer\u2019s disease classification","volume":"12","author":"Aparna","year":"2023","journal-title":"IAES Int J Artif Intell"},{"key":"B15","doi-asserted-by":"publisher","first-page":"2140012","DOI":"10.1142\/S021946782140012X","article-title":"Detection of Alzheimer\u2019s disease using deep convolutional neural network","volume":"22","author":"Kaur","year":"2022","journal-title":"Int J Image Graphics"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0302358","article-title":"A bilateral filtering-based image enhancement for Alzheimer disease classification using CNN","volume":"19","author":"Awarayi","year":"2024","journal-title":"PloS One"},{"key":"B17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.23956\/ijarcsse\/V7I6\/0259","article-title":"Deep neural network classification method to Alzheimer\u2019s disease detection","volume":"7","author":"Gulhare","year":"2017","journal-title":"Int J Adv Res Comput Sci Softw Eng"},{"key":"B18","doi-asserted-by":"crossref","DOI":"10.1007\/s11760-023-02945-w","article-title":"CNN-based Alzheimer\u2019s disease classification using fusion of multiple 3D angular orientations","volume":"18","author":"Uyguro\u011flu","year":"2024","journal-title":"Signal Image Video Process"},{"key":"B19","doi-asserted-by":"publisher","first-page":"101940","DOI":"10.1016\/j.jksuci.2024.101940","article-title":"Classification of Alzheimer\u2019s disease using MRI data based on Deep Learning Techniques","volume":"36","author":"Sorour","year":"2024","journal-title":"J King Saud University-Computer Inf Sci"},{"key":"B20","doi-asserted-by":"publisher","first-page":"469","DOI":"10.3390\/electronics12020469","article-title":"A novel framework for classification of different Alzheimer\u2019s disease stages using CNN model","volume":"12","author":"Mohi ud din dar","year":"2023","journal-title":"Electronics"},{"key":"B21","doi-asserted-by":"publisher","first-page":"3463","DOI":"10.1038\/s41598-024-53733-6","article-title":"A novel CNN architecture for accurate early detection and classification of Alzheimer\u2019s disease using MRI data","volume":"14","author":"El-Assy","year":"2024","journal-title":"Sci Rep"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1344","DOI":"10.3390\/diagnostics14131344","article-title":"AI-driven real-time classification of ECG signals for cardiac monitoring using i-alexNet architecture","volume":"14","author":"Kolhar","year":"2024","journal-title":"Diagnostics"},{"key":"B23","doi-asserted-by":"publisher","first-page":"110657","DOI":"10.1016\/j.patcog.2024.110657","article-title":"GoogLeNet-AL: A fully automated adaptive model for lung cancer detection","author":"Ma","year":"2024","journal-title":"Pattern Recognition"},{"key":"B24","doi-asserted-by":"publisher","first-page":"100626","DOI":"10.1016\/j.jbo.2024.100626","article-title":"An enhanced AlexNet-Based model for femoral bone tumor classification and diagnosis using magnetic resonance imaging","author":"Chen","year":"2024","journal-title":"J Bone Oncol"},{"key":"B25","doi-asserted-by":"publisher","first-page":"105012","DOI":"10.1016\/j.imavis.2024.105012","article-title":"Comparison of fine-tuning strategies for transfer learning in medical image classification","volume":"146","author":"Davila","year":"2024","journal-title":"Image Vision Computing"},{"key":"B26","doi-asserted-by":"publisher","first-page":"699","DOI":"10.3390\/make6010033","article-title":"Medical image classifications using convolutional neural networks: A survey of current methods and statistical modeling of the literature","volume":"6","author":"Mohammed","year":"2024","journal-title":"Mach Learn Knowledge Extraction"},{"key":"B27","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1186\/s12880-024-01195-7","article-title":"A hybrid deep CNN model for brain tumor image multi-classification","volume":"24","author":"Srinivasan","year":"2024","journal-title":"BMC Med Imaging"},{"key":"B28","doi-asserted-by":"publisher","DOI":"10.3389\/fnins.2022.1050777","article-title":"A transfer learning approach for multiclass classification of Alzheimer's disease using MRI images","volume":"16","author":"Khan","year":"2023","journal-title":"Front Neurosci"},{"key":"B29","first-page":"070441","article-title":"Deepad: Alzheimer\u2019s disease classification via deep convolutional neural networks using mri and fmri","author":"Sarraf","year":"2017","journal-title":"bioRxiv"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.1016\/j.mri.2021.02.001","article-title":"A 3D densely connected convolution neural network with connection-wise attention mechanism for Alzheimer's disease classification","volume":"78","author":"Zhang","year":"2021","journal-title":"Magnetic Resonance Imaging"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05596-x","article-title":"Genetic algorithm with logistic regression feature selection for Alzheimer\u2019s disease classification","volume":"33","author":"Divya","year":"2021","journal-title":"Neural Computing Appl"},{"key":"B32","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3131741","article-title":"An improved LeNet-deep neural network model for Alzheimer\u2019s disease classification using brain magnetic resonance images","volume":"9","author":"Hazarika","year":"2021","journal-title":"IEEE Access"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3244952","article-title":"AlzheimerNet: An effective deep learning based proposition for alzheimer\u2019s disease stages classification from functional brain changes in magnetic resonance images","volume":"11","author":"Shamrat","year":"2023","journal-title":"IEEE Access"},{"key":"B34","article-title":"Classification of Alzheimer\u2019s disease using ensemble of deep neural networks trained through transfer learning","volume":"26","author":"Tanveer","year":"2021","journal-title":"IEEE J Biomed Health Inf"},{"key":"B35","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1186\/s40708-023-00184-w","article-title":"Four-way classification of Alzheimer\u2019s disease using deep Siamese convolutional neural network with triplet-loss function","volume":"10","author":"Hajamohideen","year":"2023","journal-title":"Brain Inf"},{"key":"B36","doi-asserted-by":"publisher","first-page":"8996","DOI":"10.1038\/s41598-024-59578-3","article-title":"Joint transformer architecture in brain 3D MRI classification: its application in Alzheimer\u2019s disease classification","volume":"14","author":"Alp","year":"2024","journal-title":"Sci Rep"},{"key":"B37","doi-asserted-by":"publisher","first-page":"2450029","DOI":"10.1142\/S0129065724500291","article-title":"Performance evaluation of deep, shallow and ensemble machine learning methods for the automated classification of Alzheimer\u2019s disease","author":"Shaffi","year":"2024","journal-title":"Int J Neural Syst"},{"key":"B38","article-title":"Acknowledgement list for adni publications","author":"Weiner","year":""},{"key":"B39","doi-asserted-by":"publisher","DOI":"10.26599\/BDMA.2022.9020049","article-title":"Diagnosis and detection of Alzheimer's disease using learning algorithm","volume":"6","author":"Shukla","year":"2023","journal-title":"Big Data Min Analytics"},{"key":"B40","volume-title":"Alzheimer mri preprocessed dataset","author":"Kumar","year":"2022"},{"key":"B41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s10586-024-04618-w","article-title":"Catch fish optimization algorithm: a new human behavior algorithm for solving clustering problems","author":"Jia","year":"2024","journal-title":"Cluster Computing"},{"key":"B42","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s40846-023-00801-3","article-title":"Explainable deep-learning-based diagnosis of Alzheimer\u2019s disease using multimodal input fusion of PET and MRI Images","volume":"43","author":"Odusami","year":"2023","journal-title":"J Med Biol Eng"},{"key":"B43","doi-asserted-by":"publisher","first-page":"801","DOI":"10.3390\/diagnostics13040801","article-title":"Alzheimer disease classification through transfer learning approach","volume":"13","author":"Raza","year":"2023","journal-title":"Diagnostics"},{"key":"B44","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ACCESS.2024.3381956","article-title":"Adversarial network-based classification for alzheimer\u2019s disease using multimodal brain images: A critical analysis","volume":"15","author":"Gupta","year":"2024","journal-title":"IEEE Access"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2024.1501742\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T05:10:41Z","timestamp":1733202641000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2024.1501742\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,3]]},"references-count":44,"alternative-id":["10.3389\/fonc.2024.1501742"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2024.1501742","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,3]]},"article-number":"1501742"}}