Melanoma is considered to be one of the most dangerous human malignancy, which is diagnosed visually or by dermoscopic analysis and histopathological examination. However, as these traditional methods are based on human experience and implemented manually, there have been great limitations for general usability in current clinical practice. In this paper, a novel hybrid machine learning approach is proposed to identify melanoma for skin healthcare in various cases. The proposed approach consists of classic machine learning methods, including convolutional neural networks (CNNs), EfficientNet, and XGBoost supervised machine learning. In the proposed approach, a deep learning model is trained directly from raw pixels and image labels for classification of skin lesions. Then, solely based on modeling of various features from patients, an XGBoost model is adopted to predict skin cancer. Following that, a diagnostic system which composed of the deep learning model and XGBoost model is developed to further improve the prediction efficiency and accuracy. Different from experience-based methods and solely image-based machine learning methods, the proposed approach is developed based on the theory of deep learning and feature engineering. Experiments show that the hybrid model outperforms single model like the traditional deep learning model or XGBoost model. Moreover, the data-driven-based characteristics can help the proposed approach develop a guideline for image analysis in other medical applications.

Artificial intelligence technology is a new technology based on computer science and technology, combined with Internet technology. It has been widely used in all walks of life. With the development of information technology, the coverage of the Internet is wider, more and more users are involved, and the transmission and sharing of information is more and more convenient. This makes people’s request for information security rise to a higher level. The arrival of the big data era has put forward higher requirements for the security protection of the information technology security personnel for the complex data. Artificial intelligence technology can solve this problem well in the application of big data security defense. Artificial intelligence technology can not only enhance the security of network information, but also will change the way people live in the future with the wider application in the network, which will profoundly affect people’s life in the information age.

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