| Autor: |
Jasil, S.P. Godlin, Ulagamuthalvi, V. |
| Zdroj: |
International Journal of Public Sector Performance Management; 2024, Vol. 13 Issue: 3 p436-442, 7p |
| Abstrakt: |
In recent years, skin cancer is one of most deadly diseases in the world. Early detection of skin cancer leads to the advanced treatment. Skin cancer is diagnosed by dermatoscopic analysis, histopathological assessment and biopsy. Since the skin lesion appears very similar when comparing with the surrounding skin, classification becomes a challenging process. Due to the development in automated systems, classification of skin lesions can be done with the higher accuracy. This paper presents a deep learning neural network architecture used for automatic skin classification. Our dataset consists of 1,456 images of seven classes which is taken from HAM1000 dataset. Preprocessing such as data argumentation is done to improve the performance. The proposed architecture gives the higher classification rate with accuracy. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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