{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:59:53Z","timestamp":1777705193045,"version":"3.51.4"},"reference-count":24,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2024,4,18]]},"abstract":"<jats:p>In the wake of the global spread of monkeypox, accurate disease recognition has become crucial. This study introduces an improved SE-InceptionV3 model, embedding the SENet module and incorporating L2 regularization into the InceptionV3 framework to enhance monkeypox disease detection. Utilizing the Kaggle monkeypox dataset, which includes images of monkeypox and similar skin conditions, our model demonstrates a noteworthy accuracy of 96.71% on the test set, outperforming conventional methods and deep learning models. The SENet module\u2019s channel attention mechanism significantly elevates feature representation, while L2 regularization ensures robust generalization. Extensive experiments validate the model\u2019s superiority in precision, recall, and F1 score, highlighting its effectiveness in differentiating monkeypox lesions in diverse and complex cases. The study not only provides insights into the application of advanced CNN architectures in medical diagnostics but also opens avenues for further research in model optimization and hyperparameter tuning for enhanced disease recognition.<\/jats:p>","DOI":"10.3233\/jifs-237232","type":"journal-article","created":{"date-parts":[[2024,2,23]],"date-time":"2024-02-23T11:32:18Z","timestamp":1708687938000},"page":"8811-8828","source":"Crossref","is-referenced-by-count":7,"title":["Monkeypox disease recognition model based on improved SE-InceptionV3"],"prefix":"10.1177","volume":"46","author":[{"given":"Junzhuo","family":"Chen","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin, China"}]},{"given":"Zonghan","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering, Hebei University of Technology, Tianjin, China"}]},{"given":"Shitong","family":"Kang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-237232_ref1","doi-asserted-by":"crossref","first-page":"102855","DOI":"10.1016\/j.jaut.2022.102855","article-title":"The outbreak and the pathobiology of the monkeypox virus[J]","volume":"131","author":"Kumar","year":"2022","journal-title":"Journal of autoimmunity"},{"key":"10.3233\/JIFS-237232_ref2","doi-asserted-by":"crossref","first-page":"103979","DOI":"10.1016\/j.amsu.2022.103979","article-title":"Monkeypox-a menacing challenge or an endemic?[J]","volume":"79","author":"Fatima","year":"2022","journal-title":"Annals of Medicine and Surgery"},{"issue":"12","key":"10.3233\/JIFS-237232_ref4","doi-asserted-by":"crossref","first-page":"380","DOI":"10.3390\/v9120380","article-title":"Improving the care and treatment of monkeypox patients in low-resource settings: applying evidence from contemporary biomedical and smallpox biodefense research[J]","volume":"9","author":"Reynolds","year":"2017","journal-title":"Viruses"},{"issue":"8","key":"10.3233\/JIFS-237232_ref6","doi-asserted-by":"crossref","first-page":"440","DOI":"10.1038\/s41582-020-0377-8","article-title":"Applications of machine learning to diagnosis and treatment of neurodegenerative diseases[J]","volume":"16","author":"Myszczynska","year":"2020","journal-title":"Nature Reviews Neurology"},{"key":"10.3233\/JIFS-237232_ref7","doi-asserted-by":"crossref","first-page":"55135","DOI":"10.1109\/ACCESS.2020.2978629","article-title":"A deep learning model based on concatenation approach for the diagnosis of brain tumor[J]","volume":"8","author":"Noreen","year":"2020","journal-title":"IEEE Access"},{"key":"10.3233\/JIFS-237232_ref9","doi-asserted-by":"crossref","first-page":"100013","DOI":"10.1016\/j.ibmed.2020.100013","article-title":"Deep learning and its role in COVID-19 medical imaging[J]","volume":"3","author":"Desai","year":"2020","journal-title":"Intelligence-Based Medicine"},{"issue":"1-2","key":"10.3233\/JIFS-237232_ref10","doi-asserted-by":"crossref","first-page":"2100232","DOI":"10.1002\/pmic.202100232","article-title":"Potential of deep representative learning features to interpret the sequence information in proteomics[J]","volume":"22","author":"Le","year":"2022","journal-title":"Proteomics"},{"issue":"1","key":"10.3233\/JIFS-237232_ref11","doi-asserted-by":"crossref","first-page":"630","DOI":"10.1093\/bib\/bbac630","article-title":"Prediction of anticancer peptides based on an ensemble model of deep learning and machine learning using ordinal positional encoding[J],bbac","volume":"24","author":"Yuan","year":"2023","journal-title":"Briefings in Bioinformatics"},{"key":"10.3233\/JIFS-237232_ref13","doi-asserted-by":"crossref","first-page":"102259","DOI":"10.1016\/j.artmed.2022.102259","article-title":"Lesion-attention pyramid network for diabetic retinopathy grading[J]","volume":"126","author":"Li","year":"2022","journal-title":"Artificial Intelligence in Medicine"},{"issue":"8","key":"10.3233\/JIFS-237232_ref17","doi-asserted-by":"crossref","first-page":"1491","DOI":"10.3390\/diagnostics13081491","article-title":"Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach[J]","volume":"13","author":"Velu","year":"2023","journal-title":"Diagnostics"},{"issue":"1","key":"10.3233\/JIFS-237232_ref18","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1186\/s12879-023-08408-4","article-title":"Azar, A. 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