Journal of Korea Multimedia Society (한국멀티미디어학회논문지)

Korea Multimedia Society (한국멀티미디어학회)
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Multi-scale Attention and Deep Ensemble-Based Animal Skin Lesions Classification

다중 스케일 어텐션과 심층 앙상블 기반 동물 피부 병변 분류 기법

  • Kwak, Min Ho (Pattern Recognition and Machine Intelligence (PMI) Lab., Division of Computer Eng., Hankuk University of Foreign Studies) ;
  • Kim, Kyeong Tae (Pattern Recognition and Machine Intelligence (PMI) Lab., Division of Computer Eng., Hankuk University of Foreign Studies) ;
  • Choi, Jae Young (Pattern Recognition and Machine Intelligence (PMI) Lab., Division of Computer Eng., Hankuk University of Foreign Studies)
  • Received : 2022.07.28
  • Accepted : 2022.08.03
  • Published : 2022.08.31
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Abstract

Skin lesions are common diseases that range from skin rashes to skin cancer, which can lead to death. Note that early diagnosis of skin diseases can be important because early diagnosis of skin diseases considerably can reduce the course of treatment and the harmful effect of the disease. Recently, the development of computer-aided diagnosis (CAD) systems based on artificial intelligence has been actively made for the early diagnosis of skin diseases. In a typical CAD system, the accurate classification of skin lesion types is of great importance for improving the diagnosis performance. Motivated by this, we propose a novel deep ensemble classification with multi-scale attention networks. The proposed deep ensemble networks are jointly trained using a single loss function in an end-to-end manner. In addition, the proposed deep ensemble network is equipped with a multi-scale attention mechanism and segmentation information of the original skin input image, which improves the classification performance. To demonstrate our method, the publicly available human skin disease dataset (HAM 10000) and the private animal skin lesion dataset were used for the evaluation. Experiment results showed that the proposed methods can achieve 97.8% and 81% accuracy on each HAM10000 and animal skin lesion dataset. This research work would be useful for developing a more reliable CAD system which helps doctors early diagnose skin diseases.

Keywords

Acknowledgement

This research was supported by Hankuk University of Foreign Studies Research Fund. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT) (No. 2021R1A2C1092322).

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