Abstract
Melanoma is the most serious form of skin cancer, with more than 123,000 new cases worldwide each year. Reliable automatic melanoma screening system will be a great help for clinicians to detect malignant skin lesions as soon as possible. To address this problem, a computer-assisted approach to detecting melanoma using convolutional neural networks will enable effective and faster treatment of the disease. Many attempts have been made to use convolutional neural networks to solve this problem, but so far the performance in this aspect is not good. In this project, Xception and VGG-16 models will be fine-tuned on the IEEE International Symposium on Biomedical Imaging (ISBI) Official Skin Data Set 2016 and combined into an integrated framework to predict whether skin disease images are benign or malignant. Our experimental results show that the ensemble model can achieve better classification results and the proposed method is competitive in this field.
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Acknowledgments
This work was supported by the Natural Science Research Programme of Colleges and Universities of Anhui Province under grant KJ2020ZD39, and the Open Research Fund of Anhui Key Laboratory of Detection Technology and Energy Saving Devices under grant DTESD2020A02, the Scientific Research Project of “333 project” in Jiangsu Province under grant BRA2018218 and the Postdoctoral Research Foundation of Jiangsu Province under grant 2020Z389, and Qing Lan Project of colleges and universities in Jiangsu province.
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Shen, X., Wei, L., Lu, H., Sheng, X. (2021). Melanoma Classification Method Based on Ensemble Convolutional Neural Network. In: Fei, M., Chen, L., Ma, S., Li, X. (eds) Intelligent Life System Modelling, Image Processing and Analysis. LSMS ICSEE 2021 2021. Communications in Computer and Information Science, vol 1467. Springer, Singapore. https://doi.org/10.1007/978-981-16-7207-1_13
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DOI: https://doi.org/10.1007/978-981-16-7207-1_13
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