Using CNN model to diagnose skin cancer

Zhenyu Wu; Haoyuan Liu

doi:10.1117/12.2623115

1 December 2021 Using CNN model to diagnose skin cancer

Zhenyu Wu, Haoyuan Liu

Proceedings Volume 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering; 120791B (2021) https://doi.org/10.1117/12.2623115
Event: 2nd IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 2021, Xi'an, China

Abstract

The main idea of the project is to utilize machine learning to try to make a diagnose of potential skin cancer. We used a traditional CNN model and later a ResNet Model to solve the problem. The model takes an image of the skin sample as input and tries to predict whether the abnormality on the surface of the skin is malignant or benign. The traditional CNN model resulted in an accuracy of 81.3% while the ResNet model resulted in an accuracy of 87.7%, which is a great leap due to the advancement in ResNet model compared to a traditional CNN model. However, we concluded that the accuracy of the model can still be increased by updating come factors of the model like the size of the dataset, the computing ability, and the structure of the model itself. Besides, the researchers implemented a GUI so that the users can utilize our research more conveniently.

Citation Download Citation

Zhenyu Wu and Haoyuan Liu "Using CNN model to diagnose skin cancer", Proc. SPIE 12079, Second IYSF Academic Symposium on Artificial Intelligence and Computer Engineering, 120791B (1 December 2021); https://doi.org/10.1117/12.2623115

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