[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123360-en":3,"doc-seo-123360-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123360,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Segmenting and classifying skin lesions using a fruit fly optimization algorithm with a machine learning framework","Melanoma, one of the deadliest skin cancers, has high fatality risk and motivates more accurate computer-aided diagnosis. Existing automated approaches for skin lesion detection have not yet delivered sufficient accuracy. This research develops an improved machine learning framework to segment and classify skin lesions by using fruit fly optimization to enhance key SVM variables, forming an FOA-SVM model. The integrated approach aims to increase diagnostic accuracy and provide informative data to support more reliable classification.","Automatika  \nJournal for Control, Measurement, Electronics, Computing and Communications  \nISSN: (Print) (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/taut20)[www.tandfonline.com/journals/taut20](homepage: www.tandfonline.com/journals/taut20)  \nSegmenting and classifying skin lesions using a fruit fly optimization algorithm with a machine learning framework  \nR. Sonia, Jesla Joseph, D. Kala iyarasi, N. Kalyani, Amara S. A. L. G. Gopala Gupta, G. Ramkumar, Hesham S. Almoallim, Sulaiman Ali Alharbi & S.S. Raghavan  \nTo cite this article: R. Sonia, Jesla Joseph, D. Kala iyarasi, N. Kalyani, Amara S. A. L. G. Gopala Gupta, G. Ramkumar, Hesham S. Almoallim, Sulaiman Ali Alharbi & S.S. Raghavan (2024) Segmenting and classifying skin lesions using a fruit fly optimization algorithm with a machine learning framework, Automatika, 65: 1, 217-231, DOI: 10.1080/00051144.2023.2293515  \nTo link to this article: [https://doi.org/10.1080/00051](https://doi.org/10.1080/00051)144.2023.2293515  \n© 2023 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group.  \n Published online: 26 Dec 2023.  \n\n|  Submit your article to this journal  |\n| --- |\n|  View related articles  |\n|  Citing articles: 17 View citing articles  |\n\n\n|  Article views: 1310 |\n| --- |\n|  View Crossmark data |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=taut20](https://www.tandfonline.com/action/journalInformation?journalCode=taut20)  \nSegmenting and classifying skin lesions using a fruit fly optimization algorithm with a machine learning framework  \nR. Soniaa, Jesla Josephb, D. Kalaiyarasic, N. Kalyanid, Amara S. A. L. G. Gopala Guptae, G. Ramkumar f, Hesham S. Almoallimg, Sulaiman Ali Alharbih and S.S. Raghavani  \na Department of Computer Applications, B. S. Abdur Rahman Crescent Institute of Science and Technology, Chennai, India;b School of CSA, REVA University, Bangalore, India; c Department of Electronics and Communication Engineering, Panimalar Engineering College, Chennai, India;d Department of Computer Science and Engineering, R. M. K College of Engineering and Technology, Thiruvallur, India; e Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, India;fDepartment of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, India; g Department of Oral and Maxillofacial Surgery, College of Dentistry, King Saud University, Riyadh, Saudi Arabia;h Department of Botany and Microbiology, College of Science, King Saud University, Riyadh, Saudi Arabia;iDepartment of Health Sciences, University of Texas Health Science Center, Tyler, USA  \nABSTRACT  \nThe deadliest forms of skin cancer, melanomas have a large fatality rate. In the United States of America, 196,060 new cases of melanoma are anticipated in 2020 . In the past, many automated methods for diagnosing skin lesions have been proposed, but they have not yet proven to be very accurate. Based on skin cells’ exposure to sunlight, aberrant skin cell development frequently results in skin cancer. Ultraviolet radiation, viruses, bacteria, chemicals, and fungi are the main contributors to skin conditions. The creation of a precise computer-aided system for diagnosing breast cancer is of tremendous clinical importance. An improved machine learning framework has been developed in this research to detect skin lesions or skin cancer. Hence it is important to segment and classify the skin lesion. The research utilizes the fruit fly optimization algorithm and machine learning framework to segment and classifies skin disease and cancer. This platform’s central idea is to use the fruit fly optimization algorithm (FOA) to improve two crucial SVM variables and create an FOA-based SVM (FOA-SVM) for the diagnosis of skin cancer. The integrative approach not only improves accuracy but also ","cbCaiqWdURFPvCma","https://ap.wps.com/l/cbCaiqWdURFPvCma","pdf",2668728,1,16,"English","en",105,"# Abstract\n# Introduction\n## Skin cancer background and motivation\n# Methodology\n## Fruit fly optimization and FOA-SVM concept\n# Results and discussion\n## Segmentation and classification performance\n# Conclusion","[{\"question\":\"What problem does the research address?\",\"answer\":\"The research targets accurate segmentation and classification of skin lesions for melanoma diagnosis, since automated methods have not achieved sufficient accuracy.\"},{\"question\":\"How does the proposed framework work?\",\"answer\":\"It uses a fruit fly optimization algorithm to improve key SVM variables, creating an FOA-SVM model for diagnosis.\"},{\"question\":\"What is the expected benefit of the integrative approach?\",\"answer\":\"The approach is designed to improve accuracy and generate important data that supports more reliable classification.\"}]","Segmenting and classifying skin lesions using a fruit fly optimization algorithm with a machine learning framework | 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