Detailed Information

Cited 85 time in webofscience Cited 120 time in scopus
Metadata Downloads

Multi-Class Skin Lesion Detection and Classification via Teledermatology

Full metadata record
DC Field Value Language
dc.contributor.authorKhan, MA[Khan, Muhammad Attique]-
dc.contributor.authorMUHAMMAD, K.[MUHAMMAD, KHAN]-
dc.contributor.authorSharif, M[Sharif, Muhammad]-
dc.contributor.authorAkram, T[Akram, Tallha]-
dc.contributor.authorde Albuquerque, VHC[de Albuquerque, Victor Hugo C.]-
dc.date.accessioned2021-12-29T02:52:10Z-
dc.date.available2021-12-29T02:52:10Z-
dc.date.created2021-12-29-
dc.date.issued2021-12-
dc.identifier.issn2168-2194-
dc.identifier.urihttps://scholarx.skku.edu/handle/2021.sw.skku/94146-
dc.description.abstractTeledermatology is one of the most illustrious applications of telemedicine and e-health. In this field, telecommunication technologies are utilized to transfer medical information to the experts. Due to the skin's visual nature, teledermatology is an effective tool for the diagnosis of skin lesions especially in rural areas. Furthermore, it can also be useful to limit gratuitous clinical referrals and triage dermatology cases. The objective of this research is to classify the skin lesion image samples, received from different servers. The proposed framework is comprised of two module, which include the skin lesion localization/segmentation and the classification. In the localization module, we propose a hybrid strategy that fuses the binary images generated from the designed 16-layered convolutional neural network model and an improved high dimension contrast transform (HDCT) based saliency segmentation. To utilize maximum information extracted from the binary images, a maximal mutual information method is proposed, which returns the segmented RGB lesion image. In the classification module, a pre-trained DenseNet201 model is re-trained on the segmented lesion images using transfer learning. Afterward, the extracted features from the two fully connected layers are down-sampled using the t-distribution stochastic neighbor embedding (t-SNE) method. These resultant features are finally fused using a multi canonical correlation (MCCA) approach and are passed to a multi-class ELM classifier. Four datasets (i.e., ISBI2016, ISIC2017, PH2, and ISBI2018) are employed for the evaluation of the segmentation task, while HAM10000, the most challenging dataset, is used for the classification task. The experimental results in comparison with the state-of-the-art methods affirm the strength of our proposed framework.-
dc.language영어-
dc.language.isoen-
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC-
dc.titleMulti-Class Skin Lesion Detection and Classification via Teledermatology-
dc.typeArticle-
dc.contributor.affiliatedAuthorMUHAMMAD, K.[MUHAMMAD, KHAN]-
dc.identifier.doi10.1109/JBHI.2021.3067789-
dc.identifier.scopusid2-s2.0-85103258651-
dc.identifier.wosid000728140900008-
dc.identifier.bibliographicCitationIEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS, v.25, no.12, pp.4267 - 4275-
dc.relation.isPartOfIEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS-
dc.citation.titleIEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS-
dc.citation.volume25-
dc.citation.number12-
dc.citation.startPage4267-
dc.citation.endPage4275-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaMathematical & Computational Biology-
dc.relation.journalResearchAreaMedical Informatics-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryComputer Science, Interdisciplinary Applications-
dc.relation.journalWebOfScienceCategoryMathematical & Computational Biology-
dc.relation.journalWebOfScienceCategoryMedical Informatics-
dc.subject.keywordAuthorLesions-
dc.subject.keywordAuthorSkin-
dc.subject.keywordAuthorImage segmentation-
dc.subject.keywordAuthorMelanoma-
dc.subject.keywordAuthorFeature extraction-
dc.subject.keywordAuthorTask analysis-
dc.subject.keywordAuthorLocation awareness-
dc.subject.keywordAuthorSkin cancer-
dc.subject.keywordAuthorDermoscopy Imaging-
dc.subject.keywordAuthorlesion localization-
dc.subject.keywordAuthorfeatures reduction-
dc.subject.keywordAuthorfeatures fusion-
dc.subject.keywordAuthorteledermatology-
Files in This Item
There are no files associated with this item.
Appears in
Collections
Computing and Informatics > Convergence > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher MUHAMMAD, KHAN photo

MUHAMMAD, KHAN
Computing and Informatics (Convergence)
Read more

Altmetrics

Total Views & Downloads

BROWSE