Visualizing CoAtNet Predictions for Aiding Melanoma Detection

Abstract

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.

Radiobioinformatics: A novel bridge between basic research and clinical practice for clinical decision support in diffuse liver diseases

“…Wang et al [160] demonstrated that CoAtNet was effective in classifying renal parenchymal tumor subtypes and performed better than ViT. Kvak [161] used CoAtNet to classify malignant and benign melanoma and obtained an accuracy of 0.901, which is better than the accuracies obtained with other state-of-the-art algorithms. Tripathi et al [162] used CoAtNet for the cytomorphological classification of bone marrow cells and found that the CoAtNet model outperformed the EfficientNetV2 and ResNext50 models.…”