Automated Melanoma Recognition in Dermoscopic Images Based on Extreme Learning Machine (ELM)

Basit Öğe Kaydı
dc.authoridRahman, Md Mahmudur/0000-0003-0405-9088
dc.authoridALPASLAN, Nuh/0000-0002-6828-755X
dc.authorwosidRahman, Md Mahmudur/AAL-3192-2020
dc.authorwosidALPASLAN, Nuh/AAA-4227-2022
dc.contributor.authorRahman, Md Mahmudur
dc.contributor.authorAlpaslan, Nuh
dc.date.accessioned2024-08-04T20:43:11Z
dc.date.available2024-08-04T20:43:11Z
dc.date.issued2017
dc.departmentİnönü Üniversitesien_US
dc.descriptionConference on Medical Imaging - Computer-Aided Diagnosis -- FEB 13-16, 2017 -- Orlando, FLen_US
dc.description.abstractMelanoma is considered a major health problem since it is the deadliest form of skin cancer. The early diagnosis through periodic screening with dermoscopic images can significantly improve the survival rate as well as reduce the treatment cost and consequent suffering of patients. Dermoscopy or skin surface microscopy provides in vivo inspection of color and morphologic structures of pigmented skin lesions (PSLs), rendering higher accuracy for detecting suspicious cases than it is possible via inspecting with naked eye. However, interpretation of dermoscopic images is time consuming and subjective, even for trained dermatologists. Therefore, there is currently a great interest in the development of computer-aided diagnosis (CAD) systems for automated melanoma recognition. However, the majority of the CAD systems are still in the early development stage with lack of descriptive feature generation and benchmark evaluation in ground-truth datasets. This work is focusing on by addressing the various issues related to the development of such a CAD system with effective feature extraction from Non-Subsampled Contourlet Transform (NSCT) and Eig(Hess) histogram of oriented gradients (HOG) and lesion classification with efficient Extreme Learning Machine (ELM) due to its good generalization abilities and a high learning efficiency and evaluating its effectiveness in a benchmark data set of dermoscopic images towards the goal of realistic comparison and real clinical integration. The proposed research on melanoma recognition has huge potential for offering powerful services that would significantly benefit the present Biomedical Information Systems.en_US
dc.description.sponsorshipSPIE,Alpin Med Systen_US
dc.identifier.doi10.1117/12.2255576
dc.identifier.isbn978-1-5106-0713-2
dc.identifier.isbn978-1-5106-0714-9
dc.identifier.issn0277-786X
dc.identifier.issn1996-756X
dc.identifier.scopus2-s2.0-85020313269en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.urihttps://doi.org/10.1117/12.2255576
dc.identifier.urihttps://hdl.handle.net/11616/97848
dc.identifier.volume10134en_US
dc.identifier.wosWOS:000406425300038en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpie-Int Soc Optical Engineeringen_US
dc.relation.ispartofMedical Imaging 2017: Computer-Aided Diagnosisen_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectSkin Canceren_US
dc.subjectMelanomaen_US
dc.subjectCADen_US
dc.subjectNon-Subsampled Contourlet Transformen_US
dc.subjectClassificationen_US
dc.subjectExtreme Learning Machineen_US
dc.titleAutomated Melanoma Recognition in Dermoscopic Images Based on Extreme Learning Machine (ELM)en_US
dc.typeConference Objecten_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu