Comparative analysis of transfer learning models versus ensemble transfer learning models for multiclass cutaneous carcinoma classification
Rashmi Nair, Rohit Agrawal, Sudheesh Sudhakaran · 2025
The precise identification of various cutaneous carcinoma types relies heavily on the diagnostic proficiency and precision of dermatologists during dermal layer examinations. Cutaneous carcinomas make up a third of all cancer categories and are a significant contributor to the rising mortality rates. The proposed study compares the performance of four different transfer learning models and ensembles of different transfer learning models for cutaneous carcinoma multiclass image classification. The study shows that ensemble transfer learning models are able to more accurately perform automated multiclass image classification than different individual transfer learning models for fewer training epochs.