{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T04:33:52Z","timestamp":1772339632322,"version":"3.50.1"},"reference-count":39,"publisher":"World Scientific Pub Co Pte Ltd","issue":"02","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Neur. Syst."],"published-print":{"date-parts":[[2024,2]]},"abstract":"<jats:p> Background and Objective: Alzheimer\u2019s disease is nowadays the most common cause of dementia. It is a degenerative neurological pathology affecting the brain, progressively leading the patient to a state of total dependence, thus creating a very complex and difficult situation for the family that has to assist him\/her. Early diagnosis is a primary objective and constitutes the hope of being able to intervene in the development phase of the disease. Methods: In this paper, a method to automatically detect the presence of Alzheimer\u2019s disease, by exploiting deep learning, is proposed. Five different convolutional neural networks are considered: ALEX_NET, VGG16, FAB_CONVNET, STANDARD_CNN and FCNN. The first two networks are state-of-the-art models, while the last three are designed by authors. We classify brain images into one of the following classes: non-demented, very mild demented and mild demented. Moreover, we highlight on the image the areas symptomatic of Alzheimer presence, thus providing a visual explanation behind the model diagnosis. Results: The experimental analysis, conducted on more than 6000 magnetic resonance images, demonstrated the effectiveness of the proposed neural networks in the comparison with the state-of-the-art models in Alzheimer\u2019s disease diagnosis and localization. The best results in terms of metrics are the best with STANDARD_CNN and FCNN with accuracy, precision and recall between 98% and 95%. Excellent results also from a qualitative point of view are obtained with the Grad-CAM for localization and visual explainability. Conclusions: The analysis of the heatmaps produced by the Grad-CAM algorithm shows that in almost all cases the heatmaps highlight regions such as ventricles and cerebral cortex. Future work will focus on the realization of a network capable of analyzing the three anatomical views simultaneously. <\/jats:p>","DOI":"10.1142\/s0129065724500072","type":"journal-article","created":{"date-parts":[[2023,11,15]],"date-time":"2023-11-15T08:34:45Z","timestamp":1700037285000},"source":"Crossref","is-referenced-by-count":11,"title":["Alzheimer\u2019s Disease Evaluation Through Visual Explainability by Means of Convolutional Neural Networks"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9425-1657","authenticated-orcid":false,"given":"Francesco","family":"Mercaldo","sequence":"first","affiliation":[{"name":"Department of Medicine and Health Sciences \u201cVincenzo Tiberio\u201d, University of Molise, Campobasso, Italy"},{"name":"Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3314-7269","authenticated-orcid":false,"given":"Marcello","family":"Di Giammarco","sequence":"additional","affiliation":[{"name":"Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy"},{"name":"Department of Information Engineering, University of Pisa, Pisa, Italy"}]},{"given":"Fabrizio","family":"Ravelli","sequence":"additional","affiliation":[{"name":"Department of Medicine and Health Sciences \u201cVincenzo Tiberio\u201d, University of Molise, Campobasso, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6721-9395","authenticated-orcid":false,"given":"Fabio","family":"Martinelli","sequence":"additional","affiliation":[{"name":"Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7254-6825","authenticated-orcid":false,"given":"Antonella","family":"Santone","sequence":"additional","affiliation":[{"name":"Department of Medicine and Health Sciences \u201cVincenzo Tiberio\u201d, University of Molise, Campobasso, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9068-313X","authenticated-orcid":false,"given":"Mario","family":"Cesarelli","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Sannio, Benevento, Italy"}]}],"member":"219","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"key":"S0129065724500072BIB001","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2013.08.033"},{"key":"S0129065724500072BIB002","doi-asserted-by":"publisher","DOI":"10.1001\/jama.287.18.2335"},{"key":"S0129065724500072BIB003","doi-asserted-by":"publisher","DOI":"10.1016\/j.neulet.2008.08.008"},{"key":"S0129065724500072BIB004","doi-asserted-by":"publisher","DOI":"10.1007\/s00702-010-0450-3"},{"key":"S0129065724500072BIB005","doi-asserted-by":"publisher","DOI":"10.1097\/WAD.0b013e3181ed1160"},{"key":"S0129065724500072BIB006","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinph.2010.09.008"},{"key":"S0129065724500072BIB007","doi-asserted-by":"publisher","DOI":"10.1515\/revneuro-2013-0042"},{"key":"S0129065724500072BIB008","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065723500211"},{"key":"S0129065724500072BIB009","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065722500228"},{"key":"S0129065724500072BIB010","doi-asserted-by":"publisher","DOI":"10.1016\/j.clineuro.2020.106446"},{"key":"S0129065724500072BIB011","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2018.2791644"},{"key":"S0129065724500072BIB012","doi-asserted-by":"publisher","DOI":"10.1159\/000441447"},{"key":"S0129065724500072BIB013","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbr.2016.02.035"},{"key":"S0129065724500072BIB014","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065722500605"},{"key":"S0129065724500072BIB015","doi-asserted-by":"publisher","DOI":"10.3390\/app122312025"},{"key":"S0129065724500072BIB016","doi-asserted-by":"publisher","DOI":"10.1016\/j.jneumeth.2011.01.027"},{"key":"S0129065724500072BIB017","doi-asserted-by":"publisher","DOI":"10.3233\/JAD-2005-7301"},{"key":"S0129065724500072BIB018","doi-asserted-by":"publisher","DOI":"10.1177\/155005940503600303"},{"key":"S0129065724500072BIB019","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065722500423"},{"key":"S0129065724500072BIB020","doi-asserted-by":"publisher","DOI":"10.1142\/S0129065723500156"},{"key":"S0129065724500072BIB021","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-24801-6_10"},{"key":"S0129065724500072BIB022","first-page":"1","volume-title":"2017 IEEE Int. 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