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Melanoma is considered to be the most aggressive form of skin cancer. Due tothe similar shape of malignant and benign cancerous lesions, doctors spendconsiderably more time when diagnosing these findings. At present, theevaluation of malignancy is performed primarily by invasive histologicalexamination of the suspicious lesion. Developing an accurate classifier forearly and efficient detection can minimize and monitor the harmful effects ofskin cancer and increase patient survival rates. This paper proposes amulti-class classification task using the CoAtNet architecture, a hybrid modelthat combines the depthwise convolution matrix operation of traditionalconvolutional neural networks with the strengths of Transformer models andself-attention mechanics to achieve better generalization and capacity. Theproposed multi-class classifier achieves an overall precision of 0.901, recall0.895, and AP 0.923, indicating high performance compared to otherstate-of-the-art networks.

Visualizing CoAtNet Predictions for Aiding Melanoma Detection