• Ritul Sharma

Skin cancer is one of the most common forms of cancer globally, with melanoma being the deadliest type. Early detection and accurate classification of skin lesions are crucial for successful treatment and prognosis. In recent years, deep learning techniques have shown promising results in automating the detection and classification of skin lesions from dermoscopic images. This paper provides a comprehensive review of recent advances in deep learning-based approaches for skin cancer detection and classification. We discuss various deep learning architectures, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants, employed in different stages of skin cancer diagnosis. Furthermore, we explore the datasets used for training and evaluation, as well as the preprocessing techniques applied to enhance the performance of deep learning models.

python, ml, deep learning, convolutional neural networks (CNNs), recurrent neural networks (RNNs).

"SKIN CANCER DETECTION AND CLASSIFICATION USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.144-151, June-2024, Available :http://www.jetir.org/papers/JETIRGI06024.pdf

"SKIN CANCER DETECTION AND CLASSIFICATION USING DEEP LEARNING", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pp144-151, June-2024, Available at : http://www.jetir.org/papers/JETIRGI06024.pdf