{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:03:38Z","timestamp":1777705418733,"version":"3.51.4"},"reference-count":19,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,3,9]]},"abstract":"<jats:p>Melanoma is one of the widespread skin cancers that has affected millions in past decades. Detection of skin cancer at preliminary stages may become a source of reducing mortality rates. Hence, it is required to develop an autonomous system of reliable type for the detection of melanoma via image processing. This paper develops an independent medical imaging technique using Self-Attention Adaptation Generative Adversarial Network (SAAGAN). The entire processing model involves the process of pre-processing, feature extraction using Scale Invariant Feature Transform (SIFT), and finally, classification using SAAGAN. The simulation is conducted on ISIC 2016\/PH2 datasets, where 10-fold cross-validation is undertaken on a high-end computing platform. The simulation is performed to test the model efficacy against various images on several performance metrics that include accuracy, precision, recall, f-measure, percentage error, Matthews Correlation Coefficient, and Jaccard Index. The simulation shows that the proposed SAAGAN is more effective in detecting the test images than the existing GAN protocols.<\/jats:p>","DOI":"10.3233\/jifs-220015","type":"journal-article","created":{"date-parts":[[2022,9,2]],"date-time":"2022-09-02T11:25:52Z","timestamp":1662117952000},"page":"4113-4122","source":"Crossref","is-referenced-by-count":1,"title":["Improved self-attention generative adversarial adaptation network-based melanoma classification"],"prefix":"10.1177","volume":"44","author":[{"given":"S.","family":"Gowthami","sequence":"first","affiliation":[{"name":"Department of Biomedical Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India"}]},{"given":"R.","family":"Harikumar","sequence":"additional","affiliation":[{"name":"Electronics and Communication Engineering, Bannari Amman Institute of Technology, Sathyamangalam, India"}]}],"member":"179","reference":[{"issue":"1","key":"10.3233\/JIFS-220015_ref1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.3390\/biomedicines6010006","article-title":"Non-melanoma skin cancer pathogenesis overview","volume":"6","author":"Didona","year":"2018","journal-title":"Biomedicines"},{"issue":"1","key":"10.3233\/JIFS-220015_ref5","first-page":"1","article-title":"Predictingnon-melanoma skin cancer via amulti-parameterized artificial neural network","volume":"8","author":"Roffman","year":"2018","journal-title":"ScientificReports"},{"issue":"3","key":"10.3233\/JIFS-220015_ref6","doi-asserted-by":"crossref","first-page":"1327","DOI":"10.3892\/or.2017.5817","article-title":"Neuroendocrine factors: Themissing link in non-melanoma skin cancer","volume":"38","author":"Lupu","year":"2017","journal-title":"Oncology Reports"},{"issue":"6","key":"10.3233\/JIFS-220015_ref7","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1080\/15459624.2018.1447118","article-title":"The economic burden of occupational non-melanoma skin cancer due to solar radiation","volume":"15","author":"Mofidi","year":"2018","journal-title":"Journal of Occupational and Environmental Hygiene"},{"key":"10.3233\/JIFS-220015_ref8","doi-asserted-by":"crossref","first-page":"243","DOI":"10.1016\/j.tiv.2018.06.021","article-title":"Oleocanthal and oleacein contribute to the therapeutic potential of extra virgin oil-derived extracts in non-melanoma skin cancer","volume":"52","author":"Polini","year":"2018","journal-title":"Toxicology in Vitro"},{"key":"10.3233\/JIFS-220015_ref9","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1016\/j.envint.2018.09.039","article-title":"WHO\/ILO work-related burden of disease and injury: Protocol for systematic reviews of occupational exposure to solar ultraviolet radiation and of the effect of occupational exposure to solar ultraviolet radiation on melanoma and non-melanoma skin cancer","volume":"126","author":"Paulo","year":"2019","journal-title":"Environment International"},{"key":"10.3233\/JIFS-220015_ref10","unstructured":"Karras T. , Aittala M. , Laine S. , H\u00e4rk\u00f6nen E. , Hellsten J. , Lehtinen J. and Aila T. , Alias-free generative adversarial networks, Advances in Neural Information Processing Systems 34 (2021)."},{"issue":"8","key":"10.3233\/JIFS-220015_ref12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3463475","article-title":"A survey on generative adversarial networks: Variants, applications, and training","volume":"54","author":"Jabbar","year":"2021","journal-title":"ACM Computing Surveys (CSUR)"},{"key":"10.3233\/JIFS-220015_ref13","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.neunet.2020.09.001","article-title":"FPGAN: Face de-identification method with generative adversarial networks for social robots","volume":"133","author":"Lin","year":"2021","journal-title":"Neural Networks"},{"issue":"14","key":"10.3233\/JIFS-220015_ref14","doi-asserted-by":"crossref","first-page":"140502","DOI":"10.1103\/PhysRevLett.127.140502","article-title":"Quantum state tomography with conditional generative adversarial networks","volume":"127","author":"Ahmed","year":"2021","journal-title":"Physical Review Letters"},{"key":"10.3233\/JIFS-220015_ref15","unstructured":"Goodfellow I. , Pouget-Abadie J. , Mirza M. , Xu B. , Warde-Farley D. , Ozair... S. and Bengio Y. , Generative adversarial nets, Advances in Neural Information Processing Systems 27 (2014)."},{"key":"10.3233\/JIFS-220015_ref17","doi-asserted-by":"crossref","first-page":"106019","DOI":"10.1016\/j.cmpb.2021.106019","article-title":"Mass Image Synthesis in Mammogram with Contextual Information Based on GANs","volume":"202","author":"Shen","year":"2021","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"11","key":"10.3233\/JIFS-220015_ref18","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","article-title":"Generative adversarial networks","volume":"63","author":"Goodfellow","year":"2020","journal-title":"Communications of the ACM"},{"key":"10.3233\/JIFS-220015_ref19","doi-asserted-by":"crossref","first-page":"69","DOI":"10.1016\/j.neunet.2020.10.004","article-title":"CEGAN: Classification Enhancement Generative Adversarial Networks for unraveling data imbalance problems","volume":"133","author":"Suh","year":"2021","journal-title":"Neural Networks"},{"key":"10.3233\/JIFS-220015_ref20","doi-asserted-by":"crossref","first-page":"106018","DOI":"10.1016\/j.cmpb.2021.106018","article-title":"Semi-supervised GAN-based Radiomics Model for Data Augmentation in Breast Ultrasound Mass Classification","volume":"203","author":"Pang","year":"2021","journal-title":"Computer Methods and Programs in Biomedicine"},{"issue":"17","key":"10.3233\/JIFS-220015_ref24","doi-asserted-by":"crossref","first-page":"eaaz4169","DOI":"10.1126\/sciadv.aaz4169","article-title":"Designing complex architectured materials with generative adversarial networks","volume":"6","author":"Mao","year":"2020","journal-title":"Science Advances"},{"issue":"3","key":"10.3233\/JIFS-220015_ref25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3446374","article-title":"Generative Adversarial Networks (GANs) Challenges, Solutions, and Future Directions","volume":"54","author":"Saxena","year":"2021","journal-title":"ACM Computing Surveys (CSUR)"},{"issue":"3","key":"10.3233\/JIFS-220015_ref26","doi-asserted-by":"crossref","first-page":"4-es","DOI":"10.1145\/1276377.1276382","article-title":"Scene completion using millions of photographs, 4-es","volume":"26","author":"Hays","year":"2007","journal-title":"ACM Transactions on Graphics (ToG)"},{"issue":"99","key":"10.3233\/JIFS-220015_ref29","first-page":"1","article-title":"GAN-Generated Image Detection with Self-Attention Mechanism against GAN Generator Defect","volume":"PP","author":"Zhongjie","year":"2020","journal-title":"IEEE Journal of Selected Topics in Signal Processing"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-220015","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:43:46Z","timestamp":1777455826000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-220015"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,3,9]]},"references-count":19,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.3233\/jifs-220015","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,3,9]]}}}