{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T18:07:00Z","timestamp":1791914820529,"version":"4.3.4"},"reference-count":53,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T00:00:00Z","timestamp":1666224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ILMA University","award":["ILMA\/ORIC\/RJ\/2023\/1001"],"award-info":[{"award-number":["ILMA\/ORIC\/RJ\/2023\/1001"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Molecules"],"abstract":"<jats:p>Alzheimer\u2019s Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD detection and automation methods. The DL model\u2019s prediction accuracy depends on the dataset\u2019s size. The DL models lose their accuracy when the dataset has an imbalanced class problem. This study aims to use the deep Convolutional Neural Network (CNN) to develop a reliable and efficient method for identifying Alzheimer\u2019s disease using MRI. In this study, we offer a new CNN architecture for diagnosing Alzheimer\u2019s disease with a modest number of parameters, making it perfect for training a smaller dataset. This proposed model correctly separates the early stages of Alzheimer\u2019s disease and displays class activation patterns on the brain as a heat map. The proposed Detection of Alzheimer\u2019s Disease Network (DAD-Net) is developed from scratch to correctly classify the phases of Alzheimer\u2019s disease while reducing parameters and computation costs. The Kaggle MRI image dataset has a severe problem with class imbalance. Therefore, we used a synthetic oversampling technique to distribute the image throughout the classes and avoid the problem. Precision, recall, F1-score, Area Under the Curve (AUC), and loss are all used to compare the proposed DAD-Net against DEMENET and CNN Model. For accuracy, AUC, F1-score, precision, and recall, the DAD-Net achieved the following values for evaluation metrics: 99.22%, 99.91%, 99.19%, 99.30%, and 99.14%, respectively. The presented DAD-Net outperforms other state-of-the-art models in all evaluation metrics, according to the simulation results.<\/jats:p>","DOI":"10.3390\/molecules27207085","type":"journal-article","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T22:49:46Z","timestamp":1666306186000},"page":"7085","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["DAD-Net: Classification of Alzheimer\u2019s Disease Using ADASYN Oversampling Technique and Optimized Neural Network"],"prefix":"10.3390","volume":"27","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9497-5738","authenticated-orcid":false,"given":"Gulnaz","family":"Ahmed","sequence":"first","affiliation":[{"name":"Institute of Artificial Intelligence and Marine Robotics, College of Marine Electrical, Dalian Maritime University, Dalian 116000, China"},{"name":"Department of Software Engineering, ILMA University, Karachi 75000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meng Joo","family":"Er","sequence":"additional","affiliation":[{"name":"Institute of Artificial Intelligence and Marine Robotics, College of Marine Electrical, Dalian Maritime University, Dalian 116000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2911-1981","authenticated-orcid":false,"given":"Mian Muhammad Sadiq","family":"Fareed","sequence":"additional","affiliation":[{"name":"Department of Computer Science, ILMA University, Karachi 75000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7414-9826","authenticated-orcid":false,"given":"Shahid","family":"Zikria","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Information Technology University, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3917-9530","authenticated-orcid":false,"given":"Saqib","family":"Mahmood","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Rahim Yar Khan 64200, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiao","family":"He","sequence":"additional","affiliation":[{"name":"School of International Business and Management, Sichuan International Studies University, Chongqing 400031, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad","family":"Asad","sequence":"additional","affiliation":[{"name":"Graduate School of Information Science and Technology, University of Tokyo, Tokyo 113-8656, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4751-8574","authenticated-orcid":false,"given":"Syeda Fizzah","family":"Jilani","sequence":"additional","affiliation":[{"name":"Department of Physics, Physical Sciences Building, Aberystwyth University, Aberystwyth SY23 3FL, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9697-6766","authenticated-orcid":false,"given":"Muhammad","family":"Aslam","sequence":"additional","affiliation":[{"name":"School of Computing Engineering and Physical Sciences, University of West of Scotland, Glasgow G72 0LH, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e993","DOI":"10.1212\/WNL.0000000000005108","article-title":"Economic perspective of dementia care in Pakistan","volume":"90","author":"Thaver","year":"2018","journal-title":"Neurology"},{"key":"ref_2","first-page":"3\u00c010","article-title":"Neurodegeneration: What is it and where are we","volume":"111","author":"Serge","year":"2003","journal-title":"J. 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