Machine Learning Models for the Classification of Skin Cancer

Simple item page
dc.contributor.authorArooj, Sahar
dc.contributor.authorKhan, Muhammad Farhan
dc.contributor.authorKhan, Muhammad Adnan
dc.contributor.authorKhan, Muhammad Saleem
dc.contributor.authorTaleb, Nasser
dc.date.accessioned2022-05-22T09:28:20Z
dc.date.available2022-05-22T09:28:20Z
dc.date.copyright© 2022
dc.date.issued2022
dc.identifier.citationArooj, S., Khan, M. F., Khan, M. A., Khan, M. S., & Taleb, N. (2022). Machine learning models for the classification of skin cancer. 2022 International Conference on Business Analytics for Technology and Security (ICBATS). https://doi.org/10.1109/ICBATS54253.2022.9759054
dc.identifier.isbn978-166540920-9
dc.identifier.urihttps://doi.org/10.1109/ICBATS54253.2022.9759054
dc.identifier.urihttp://hdl.handle.net/20.500.12519/649
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relationAuthor Affiliations : Arooj, S., Riphah International University, Riphah School of Computing and Innovation, Faculty of Computing, Lahore, Pakistan; Khan, M.F., University of Health Sciences, Department of Forensic Sciences, Lahore, Pakistan; Khan, M.A., Gachon University, Pattern Recognition and Machine Learning Lab, Department of Software, Seongnam, 13120, South Korea; Khan, M.S., Ncba, School of Computer Science, Lahore, Pakistan; Taleb, N., Canadian University, Dubai, United Arab Emirates
dc.relation.ispartofseries2022 International Conference on Business Analytics for Technology and Security (ICBATS)
dc.rights.holderCopyright : © 2022 IEEE.
dc.rights.urihttps://www.ieee.org/publications/rights/rights-policies.html
dc.subjectComputer-aided diagnostic (CAD)
dc.subjectConvolutional neural network (CNN)
dc.subjectWorld health organization (WHO)
dc.titleMachine Learning Models for the Classification of Skin Cancer
dc.typeConference Paper
dspace.entity.type

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Access Instruction 649.pdf
Size:
104.23 KB
Format:
Adobe Portable Document Format
Description:
Download

Collections

MITGov