SKIN CANCER PREDICTION USING DEEP LEARNING

Authors

  • Mrs.M.Kavitha, D.Subramanyam, Gunda tejasree, Katam Sujitha reddy, P Sree laxmi ,Jonnada harshitha

Keywords:

Credit rating, Logistic Regression, Retail Risk modelling, Loan Default, Optimum LTV,HAM Tool

Abstract

There is a rising requirement for early determination of skin disease because of the quick development pace of melanoma, its high treatment expenses, and high death rate. Customarily, distinguishing skin disease cells has been done physically, frequently bringing about lengthy treatment processes. As of now, the fundamental difficulties in skin malignant growth discovery are high misclassification rates and low exactness.

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References

R. Ashraf et al., "Region-of-Interest Based Transfer Learning Assisted Framework for Skin Cancer Detection," IEEE Access, vol. 8, pp. 147858-147871, 2020, doi: JO.I 109/ACCESS.2020.3014701.

M. Dildar et al., "Skin cancer detection: A review using deep learning techniques," Int. J. Environ. Res. Public Health, vol. 18,

no. 10, 2021, doi: 10.3390/ijerphl8105479.

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Published

2023-12-24

How to Cite

Mrs.M.Kavitha, D.Subramanyam, Gunda tejasree, Katam Sujitha reddy, P Sree laxmi ,Jonnada harshitha. (2023). SKIN CANCER PREDICTION USING DEEP LEARNING . Pegem Journal of Education and Instruction, 13(4), 577–582. Retrieved from https://pegegog.net/index.php/pegegog/article/view/4022

Issue

Vol. 13 No. 4 (2023): PEGEGOG

Section

Article

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