<![CDATA[Skin cancer is one of the diseases whose prevalence continues to increase every year, especially in areas with high exposure to ultraviolet (UV) rays. The main challenge in diagnosing skin cancer lies in the visual similarity between benign and malignant lesions, which often leads to misdiagnosis even by experienced medical personnel. The development of deep learning technology has made significant progress in medical image classification through a transfer learning approach. This study aims to compare the performance of two architectures from Transformer and CNN, namely Swin Transformer and ConvNeXt, in the task of classifying two class benign and malignant skin cancer images. Both models use pretrained from ImageNet and are applied with three different fine-tuning strategies, namely Linear Probe (LP), Full Fine-Tuning (FT), and a combination of the two previous strategies (LP-FT). The dataset used is the ISIC Archive Dataset with an 80:20 data split for training and validation, consisting of 3.297 images divided into two classes, with 1800 benign images and 1.497 malignant images. The evaluation was performed using the accuracy, precision, recall, and F1-score metrics. Swin Transformer with the LP-FT strategy achieved the best performance, with an accuracy of 92,27%, precision of 92,24%, recall of 92,17%, and an F1-score of 92,20%. These findings indicate that the two-stage fine-tuning approach can improve model stability and generalization, as well as contribute to the development of a more accurate artificial intelligence based skin cancer diagnosis system.]]>
