{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T20:12:58Z","timestamp":1783627978405,"version":"3.55.0"},"reference-count":57,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,21]],"date-time":"2022-01-21T00:00:00Z","timestamp":1642723200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The variation in skin textures and injuries, as well as the detection and classification of skin cancer, is a difficult task. Manually detecting skin lesions from dermoscopy images is a difficult and time-consuming process. Recent advancements in the domains of the internet of things (IoT) and artificial intelligence for medical applications demonstrated improvements in both accuracy and computational time. In this paper, a new method for multiclass skin lesion classification using best deep learning feature fusion and an extreme learning machine is proposed. The proposed method includes five primary steps: image acquisition and contrast enhancement; deep learning feature extraction using transfer learning; best feature selection using hybrid whale optimization and entropy-mutual information (EMI) approach; fusion of selected features using a modified canonical correlation based approach; and, finally, extreme learning machine based classification. The feature selection step improves the system\u2019s computational efficiency and accuracy. The experiment is carried out on two publicly available datasets, HAM10000 and ISIC2018. The achieved accuracy on both datasets is 93.40 and 94.36 percent. When compared to state-of-the-art (SOTA) techniques, the proposed method\u2019s accuracy is improved. Furthermore, the proposed method is computationally efficient.<\/jats:p>","DOI":"10.3390\/s22030799","type":"journal-article","created":{"date-parts":[[2022,1,23]],"date-time":"2022-01-23T20:34:40Z","timestamp":1642970080000},"page":"799","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":145,"title":["Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine"],"prefix":"10.3390","volume":"22","author":[{"given":"Farhat","family":"Afza","sequence":"first","affiliation":[{"name":"Department of Computer Science, Wah Campus, COMSATS University Islamabad, Wah Cantt 47040, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1355-2168","authenticated-orcid":false,"given":"Muhammad","family":"Sharif","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Wah Campus, COMSATS University Islamabad, Wah Cantt 47040, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6763-2123","authenticated-orcid":false,"given":"Muhammad Attique","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, HITEC University Taxila, Taxila 47080, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7672-1187","authenticated-orcid":false,"given":"Usman","family":"Tariq","sequence":"additional","affiliation":[{"name":"College of Computer Engineering and Science, Prince Sattam Bin Abdulaziz University, Al-Kharaj 11942, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hwan-Seung","family":"Yong","sequence":"additional","affiliation":[{"name":"Department of Computer Science & Engineering, Ewha Womans University, Seoul 03760, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4869-901X","authenticated-orcid":false,"given":"Jaehyuk","family":"Cha","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Hanyang University, Seoul 04763, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Strzelecki, M.H., Str\u0105kowska, M., Koz\u0142owski, M., Urba\u0144czyk, T., Wielowieyska-Szybi\u0144ska, D., and Kocio\u0142ek, M. 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