Utilize multichannel attention amplification fusion for skin disease diagnosis

Jiawei Tang; Fengbo Zheng; Gongbo Liang; Lifen Jiang

doi:10.1117/12.2667815

28 March 2023 Utilize multichannel attention amplification fusion for skin disease diagnosis

Jiawei Tang, Fengbo Zheng, Gongbo Liang, Lifen Jiang

Proceedings Volume 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022); 125663A (2023) https://doi.org/10.1117/12.2667815
Event: Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 2022, Chongqing, China

Abstract

Patients with early-stage skin cancer have a 99% survival rate, but once it spreads, the chances of survival are poor. Deep learning has greatly improved the quality of computer-aided diagnosis of skin diseases. However, the majority of existing methods do not take into account the influence of the extracted features on the classification results. In this paper, we proposed a deep neural network with Multichannel Attention amplification Fusion (MAF) module for skin diseases classification. First, the images go through our specially designed pre-processing process which retains more basic image information. The features extracted by the deep neural network are fed into MAF which is designed to amplify the role of key feature points in the classification to improve the effectiveness of classification. Our model achieved an accuracy of 88.13% and 82.83% on the ISIC2016 dataset and ISIC2017 dataset, respectively. We also performed ablation studies which showed that our proposed pre-processing method and the MAF module effectively improve the expressiveness of the model.

Citation Download Citation

Jiawei Tang, Fengbo Zheng, Gongbo Liang, and Lifen Jiang "Utilize multichannel attention amplification fusion for skin disease diagnosis", Proc. SPIE 12566, Fifth International Conference on Computer Information Science and Artificial Intelligence (CISAI 2022), 125663A (28 March 2023); https://doi.org/10.1117/12.2667815

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