Skin lesion classification by weighted ensemble deep learning
| dc.contributor.author | Al-Saedi, Doaa Khalid Abdulridha | |
| dc.contributor.author | Savaş, Serkan | |
| dc.date.accessioned | 2025-01-21T16:28:31Z | |
| dc.date.available | 2025-01-21T16:28:31Z | |
| dc.date.issued | 2024 | |
| dc.department | Kırıkkale Üniversitesi | |
| dc.description.abstract | Skin cancer represents a significant global health threat with potentially fatal consequences if left undiagnosed. Early detection is crucial for successful patient treatment, yet accurate identification of skin lesions poses a challenge even for experienced dermatologists. In this context, the development of computer-aided skin lesion classification systems emerges as a promising path to empower dermatologists with the potential for earlier diagnoses and more effective treatment interventions. This study proposes a two-stage approach for early detection of skin cancer. Firstly, eight pre-trained deep architectures were tested on the ISIC dataset using transfer learning and fine-tuning. Secondly, three successful models with the highest accuracy were chosen, and ensemble learning was employed to obtain a final result. The ensemble learning method outperformed individual models, achieving a remarkable ROC AUC rate of 99.96%. DenseNet121 exhibited the highest performance among the individual models, with accuracy rates of 99.75%, 98.2%, and 99.6% for the train, validation, and test datasets, respectively. The promising results hold significant potential for early detection and treatment of skin cancer, a prevalent global disease. These findings could prove invaluable for clinics, offering valuable support to their decision-making processes and enhancing their ability to combat this widespread health concern. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. | |
| dc.identifier.doi | 10.1007/s42044-024-00210-y | |
| dc.identifier.endpage | 800 | |
| dc.identifier.issn | 2520-8438 | |
| dc.identifier.issue | 4 | |
| dc.identifier.scopus | 2-s2.0-85207669096 | |
| dc.identifier.scopusquality | N/A | |
| dc.identifier.startpage | 785 | |
| dc.identifier.uri | https://doi.org/10.1007/s42044-024-00210-y | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12587/23569 | |
| dc.identifier.volume | 7 | |
| dc.indekslendigikaynak | Scopus | |
| dc.language.iso | en | |
| dc.publisher | Springer International Publishing | |
| dc.relation.ispartof | Iran Journal of Computer Science | |
| dc.relation.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.snmz | KA_20241229 | |
| dc.subject | Deep learning; Ensemble learning; ISIC; Skin cancer; Skin lesion; Transfer learning | |
| dc.title | Skin lesion classification by weighted ensemble deep learning | |
| dc.type | Article |
