Convolutional Neural Network with a Novel Attention Mechanism for Skin Cancer Recognition

Xiaojing Liu, Shugen Ma, Shoufei Han, Honghao Zhu, Ruyi Han

Abstract: With the continuous development of social economy, artificial intelligence technology has gradually become an indis-pensable part of various industries, and convolutional neural network shows its powerful learning ability in the field of medical image diagnosis. In the traditional medical field, the amount of medical image data is huge, and doctors need to go through long-term professional training and rich practical experience in order to interpret it, which is a time-consuming and labour-intensive process. In contrast, convolutional neural network, with their powerful learning computational ability, can effectively handle large amounts of medical image data. This work aims to investigate the convolutional neural network algorithm to identify image data of malignant and benign skin cancers. In addition, in order to further improve its classification accuracy, a novel attention mechanism, namely input attention mechanism, is integrated into it, and the convolutional neural network model with the proposed attention mechanism is proposed. Finally, the effectiveness of the proposed model is verified on a large amount of skin image data.

External IDs:dblp:conf/icnsc/LiuMHZH24