TY - GEN
T1 - Dermoscopic Image Classification Using Attention Mechanism and Ensemble Learning Approaches
AU - Huang, Shanchuan
AU - Lei, Hongwei
AU - Jin, Liuhan
AU - Yang, Jinzhu
AU - Jiang, Tao
AU - Yao, Yudong
AU - Grzegorzek, Marcin
AU - Li, Chen
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Attention mechanism
KW - Ensemble learning
KW - Image classification
KW - Late fusion
KW - Skin Cancer
UR - http://www.scopus.com/inward/record.url?scp=85184986949&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85184986949&partnerID=8YFLogxK
U2 - 10.1109/BigData59044.2023.10386731
DO - 10.1109/BigData59044.2023.10386731
M3 - Conference contribution
AN - SCOPUS:85184986949
T3 - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
SP - 4424
EP - 4431
BT - Proceedings - 2023 IEEE International Conference on Big Data, BigData 2023
A2 - He, Jingrui
A2 - Palpanas, Themis
A2 - Hu, Xiaohua
A2 - Cuzzocrea, Alfredo
A2 - Dou, Dejing
A2 - Slezak, Dominik
A2 - Wang, Wei
A2 - Gruca, Aleksandra
A2 - Lin, Jerry Chun-Wei
A2 - Agrawal, Rakesh
T2 - 2023 IEEE International Conference on Big Data, BigData 2023
Y2 - 15 December 2023 through 18 December 2023
ER -