  Melanoma is considered to be the most aggressive form of skin cancer. Due to
the similar shape of malignant and benign cancerous lesions, doctors spend
considerably more time when diagnosing these findings. At present, the
evaluation of malignancy is performed primarily by invasive histological
examination of the suspicious lesion. Developing an accurate classifier for
early and efficient detection can minimize and monitor the harmful effects of
skin cancer and increase patient survival rates. This paper proposes a
multi-class classification task using the CoAtNet architecture, a hybrid model
that combines the depthwise convolution matrix operation of traditional
convolutional neural networks with the strengths of Transformer models and
self-attention mechanics to achieve better generalization and capacity. The
proposed multi-class classifier achieves an overall precision of 0.901, recall
0.895, and AP 0.923, indicating high performance compared to other
state-of-the-art networks.
