Article
Version 1
Preserved in Portico This version is not peer-reviewed
Visualizing CoAtNet Predictions for Aiding Melanoma Detection
Version 1
: Received: 21 May 2022 / Approved: 23 May 2022 / Online: 23 May 2022 (10:55:45 CEST)
How to cite: Kvak, D. Visualizing CoAtNet Predictions for Aiding Melanoma Detection. Preprints 2022, 2022050302. https://doi.org/10.20944/preprints202205.0302.v1 Kvak, D. Visualizing CoAtNet Predictions for Aiding Melanoma Detection. Preprints 2022, 2022050302. https://doi.org/10.20944/preprints202205.0302.v1
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.
Keywords
skin cancer; melanoma; computer-aided diagnostics; image classification; CoAtNet; convolutional neural networks; deep learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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