THE POWER OF GENERATIVE AI TO AUGMENT FOR ENHANCED SKIN CANCER CLASSIFICATION : A DEEP LERANING APPROACH

Authors

  • Mrs.N.PRASANNA KUMARI, SIRIPURAPU VARSHINI, NENAVATH DIVYA, CHALKE PRAGATHI

Keywords:

Skin Cancer, Generative AI, Deep Learning, Data Augmentation, Generative Adversarial Networks, Convolutional Neural Networks, Classification, Early Diagnosis, Healthcare, Artificial Intelligence.

Abstract

Skin cancer, encompassing melanoma and non-melanoma types, is a leading cause of cancer related deaths worldwide. Early detection is crucial for effective treatment; however, the scarcity of dermatologists, especially in rural areas, hampers timely diagnosis. Recent
advancements in artificial intelligence (AI), particularly deep learning, have shown promise in automating skin cancer classification.

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References

Esteva, A., Kuprel, B., Novoa, R.A., et al. (2017). Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), 115–118.

Rashid, R., Mahbub, U., Rahman, M.M., et al. (2021). A generative adversarial network approach for skin lesion classification. Computers in

Biology and Medicine, 133, 104399.

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Published

2023-12-23

How to Cite

Mrs.N.PRASANNA KUMARI, SIRIPURAPU VARSHINI, NENAVATH DIVYA, CHALKE PRAGATHI. (2023). THE POWER OF GENERATIVE AI TO AUGMENT FOR ENHANCED SKIN CANCER CLASSIFICATION : A DEEP LERANING APPROACH. Pegem Journal of Education and Instruction, 13(4), 553–560. Retrieved from https://www.pegegog.net/index.php/pegegog/article/view/4019

Issue

Vol. 13 No. 4 (2023): PEGEGOG

Section

Article

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