{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T15:03:18Z","timestamp":1777734198602,"version":"3.51.4"},"reference-count":47,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T00:00:00Z","timestamp":1690416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MTI"],"abstract":"<jats:p>With the widespread of Monkeypox and increase in the weekly reported number of cases, it is observed that this outbreak continues to put the human beings in risk. The early detection and reporting of this disease will help monitoring and controlling the spread of it and hence, supporting international coordination for the same. For this purpose, the aim of this paper is to classify three diseases viz. Monkeypox, Chikenpox and Measles based on provided image dataset using trained standalone DL models (InceptionV3, EfficientNet, VGG16) and Squeeze and Excitation Network (SENet) Attention model. The first step to implement this approach is to search, collect and aggregate (if require) verified existing dataset(s). To the best of our knowledge, this is the first paper which has proposed the use of SENet based attention models in the classification task of Monkeypox and also targets to aggregate two different datasets from distinct sources in order to improve the performance parameters. The unexplored SENet attention architecture is incorporated with the trunk branch of InceptionV3 (SENet+InceptionV3), EfficientNet (SENet+EfficientNet) and VGG16 (SENet+VGG16) and these architectures improve the accuracy of the Monkeypox classification task significantly. Comprehensive experiments on three datasets depict that the proposed work achieves considerably high results with regard to accuracy, precision, recall and F1-score and hence, improving the overall performance of classification. Thus, the proposed research work is advantageous in enhanced diagnosis and classification of Monkeypox that can be utilized further by healthcare experts and researchers to confront its outspread.<\/jats:p>","DOI":"10.3390\/mti7080075","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T02:10:45Z","timestamp":1690510245000},"page":"75","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["An Enhanced Diagnosis of Monkeypox Disease Using Deep Learning and a Novel Attention Model Senet on Diversified Dataset"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4381-5130","authenticated-orcid":false,"given":"Shivangi","family":"Surati","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Himani","family":"Trivedi","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, LDRP Institute of Technology and Research, Gandhinagar 382015, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bela","family":"Shrimali","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Institute of Technology, Nirma University, Ahmedabad 382481, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0423-0159","authenticated-orcid":false,"given":"Chintan","family":"Bhatt","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, School of Technology, Pandit Deendayal Energy University, Gandhinagar 382007, Gujarat, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4621-2768","authenticated-orcid":false,"given":"Carlos M.","family":"Travieso-Gonz\u00e1lez","sequence":"additional","affiliation":[{"name":"Signals and Communications Department, IDeTIC, University of Las Palmas de Gran Canaria, 35001 Las Palmas, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1014","DOI":"10.3201\/eid2206.150579","article-title":"Extended human-to-human transmission during a monkeypox outbreak in the Democratic Republic of the Congo","volume":"22","author":"Nolen","year":"2016","journal-title":"Emerg. 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