A classification method for multi-class skin damage images combining quantum computing and Inception-ResNet-V1

Melanoma is a high-grade malignant tumor. Melanoma and mole lesions are highly similar and have a very high mortality rate. Early diagnosis and treatment have an important impact on the patient's condition. The results of dermoscopy are usually judged visually by doctors through long-term clinical experience, and the diagnostic results may be different under different visual conditions. Computer-aided examinations can help doctors improve efficiency and diagnostic accuracy. The purpose of this paper is to use an improved quantum Inception-ResNet-V1 model to classify multiple types of skin lesion images and improve the accuracy of melanoma identification. In this study, the FC layer of Inception-ResNet-V1 is removed, the average pooling layer is the last, SVM is used as the classifier, and the convolutional layer is quantized. The performance of the model was tested experimentally on the ISIC 2019 dataset. To prevent the imbalance of the sample data set from affecting the experiment, the sample data is sampled with weight. Experiments show that the method used shows excellent performance, and the classification accuracy rate reaches 98%, which provides effective help for the clinical diagnosis of melanoma.