Residual Generative Adversarial Adaptation Network for the Classification of Melanoma

The capability of recognising skin cancer in its earliest stages has the potential to be a component that saves lives. It is of the utmost importance to devise an autonomous technique that can be relied upon for accurate melanoma detection using image analysis. In this paper, Generative adversarial network (GAN) with suitable preprocessing is used to classify the labels for the detection of melanoma skin types. The simulation is run to evaluate the effectiveness of the model about several performance measures, such as accuracy, precision, recall, f-measure, percentage error, Dice coefficient, and Jaccard index. These are all performance measures that are taken into consideration. These metrics for measuring achievement are as follows: The results of the simulations make it exceedingly clear that the proposed TE-SAAGAN is more effective than the existing GAN protocols when it comes to recognising the test images.

Automated summary

In this paper, a autonomous technique using Generative Adversarial Network (GAN) and suitable preprocessing is proposed for the detection of melanoma. The effectiveness of the technique is evaluated through simulation experiments, and the results show that it outperforms existing GAN protocols in recognizing test images.