Kvak, D. (2022). Visualizing CoAtNet predictions for aiding melanoma detection. arXiv preprint arXiv:2205.10515.
Abstract
Melanoma is considered to be the most aggressive form of skin cancer. 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. Due to the similar shape of malignant and benign cancerous lesions, doctors spend considerably more time when diagnosing these findings. However, using a deep learning approach as a computer vision tool can overcome some of the challenges. This paper proposes a multi-class classification task using the CoAtNet architecture, a hybrid model that combines the deep 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 model was evaluated based on precision, recall, and AP. 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. The proposed approach should provide a less complex framework to automate the melanoma diagnostic process and speed up the life-saving process.
