Dermoscopic Image Classification Using Attention Mechanism and Ensemble Learning Approaches

Shanchuan Huang, Hongwei Lei, Liuhan Jin, Jinzhu Yang, Tao Jiang, Yudong Yao, Marcin Grzegorzek, Chen Li

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Background and purpose: Skin tumours have become one of the most common diseases worldwide. While benign ones are not usually a threat to human health, malignant ones can develop into skin cancer and become life-threatening if left untreated. Early detection of the disease is important for the treatment of patients with skin tumours and dermoscopy is the most effective means of diagnosing skin tumours. However, the complexity of skin tumour cells makes the diagnosis somewhat erroneous for doctors. Therefore, a dermoscopic classification network based on deep learning and computer-aided diagnostic techniques is needed to obtain a high diagnostic accuracy rate for skin tumours. Methods: In this paper, Deep-skin, a model for dermoscopic image classification is proposed, which is based on both attention mechanism and ensemble learning. Considering the characteristics of dermoscopic images, embedding different attention mechanisms on top of Inception-V3 has been suggested to obtain more potential features. We then improve the classification performance by late fusion of the different models. To demonstrate the effectiveness of Deep-skin, experiments and evaluations are performed on the publicly available dataset Skin Cancer: Malignant vs. Benign and compare the performance of Deep-skin with other classification models. Results: The experimental results indicate that Deep-skin performs well on the dataset in comparison to other models, achieving a maximum accuracy of 87.8%.Conclusion: In this paper, the Deep-skin model is proposed for the classification of dermoscopic images and has shown better performance. In the future, we intend to investigate better classification models for automatic diagnosis of skin tumours. Such models can potentially assist physicians and patients in clinical settings.

Original languageEnglish
Title of host publicationProceedings - 2023 IEEE International Conference on Big Data, BigData 2023
EditorsJingrui He, Themis Palpanas, Xiaohua Hu, Alfredo Cuzzocrea, Dejing Dou, Dominik Slezak, Wei Wang, Aleksandra Gruca, Jerry Chun-Wei Lin, Rakesh Agrawal
Pages4424-4431
Number of pages8
ISBN (Electronic)9798350324457
DOIs
StatePublished - 2023
Event2023 IEEE International Conference on Big Data, BigData 2023 - Sorrento, Italy
Duration: 15 Dec 202318 Dec 2023

Publication series

NameProceedings - 2023 IEEE International Conference on Big Data, BigData 2023

Conference

Conference2023 IEEE International Conference on Big Data, BigData 2023
Country/TerritoryItaly
CitySorrento
Period15/12/2318/12/23

Keywords

  • Attention mechanism
  • Ensemble learning
  • Image classification
  • Late fusion
  • Skin Cancer

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