Multi-Class Skin Lesions Classification Using Deep Features

Simple item page
aut.relation.endpage8311
aut.relation.issue21en_NZ
aut.relation.journalSensorsen_NZ
aut.relation.startpage8311
aut.relation.volume22en_NZ
aut.researcherMirza, Farhaan
dc.contributor.authorUsama, Men_NZ
dc.contributor.authorNaeem, MAen_NZ
dc.contributor.authorMirza, Fen_NZ
dc.date.accessioned2022-11-03T00:48:09Z
dc.date.available2022-11-03T00:48:09Z
dc.description.abstractSkin cancer classification is a complex and time-consuming task. Existing approaches use segmentation to improve accuracy and efficiency, but due to different sizes and shapes of lesions, segmentation is not a suitable approach. In this research study, we proposed an improved automated system based on hybrid and optimal feature selections. Firstly, we balanced our dataset by applying three different transformation techniques, which include brightness, sharpening, and contrast enhancement. Secondly, we retrained two CNNs, Darknet53 and Inception V3, using transfer learning. Thirdly, the retrained models were used to extract deep features from the dataset. Lastly, optimal features were selected using moth flame optimization (MFO) to overcome the curse of dimensionality. This helped us in improving accuracy and efficiency of our model. We achieved 95.9%, 95.0%, and 95.8% on cubic SVM, quadratic SVM, and ensemble subspace discriminants, respectively. We compared our technique with state-of-the-art approach.en_NZ
dc.identifier.citationSensors, 22(21), 8311. https://doi.org/10.3390/s22218311
dc.identifier.doi10.3390/s22218311en_NZ
dc.identifier.issn1424-8220en_NZ
dc.identifier.urihttps://hdl.handle.net/10292/15589
dc.languageenen_NZ
dc.publisherMDPI AGen_NZ
dc.relation.urihttps://www.mdpi.com/1424-8220/22/21/8311en_NZ
dc.rights© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
dc.rights.accessrightsOpenAccessen_NZ
dc.subjectSkin cancer; Augmentation; Deep learning; moth flame optimization; SVM; Feature optimization; Transfer learning; Deep features
dc.titleMulti-Class Skin Lesions Classification Using Deep Featuresen_NZ
dc.typeJournal Article
pubs.elements-id482436
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies
pubs.organisational-data/AUT/Faculty of Design & Creative Technologies/School of Engineering, Computer & Mathematical Sciences
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
sensors-22-08311-v2.pdf
Size:
1.55 MB
Format:
Adobe Portable Document Format
Description:
Journal article
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
AUT Grant of Licence for Tuwhera Jun 2021.pdf
Size:
360.95 KB
Format:
Adobe Portable Document Format
Description:
Download
Collections
School of Engineering, Computer and Mathematical Sciences - Te Kura Mātai Pūhanga, Rorohiko, Pāngarau