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Skin Cancer Detection Using Deep Learning (CNN)

EasyChair Preprint no. 13422

6 pagesDate: May 23, 2024


One of the most prevalent types of cancer in the world is skin cancer, and improving patient outcomes and ensuring a successful course of treatment depend on early identification. Historically, skin lesion diagnosis has been carried out by dermatologists by visual examination, which is prone to subjectivity and individual differences in precision. Here, we suggested that Convolutional Neural Networks (CNNs) and the Deep Learning approach may be used to create a reliable skin cancer detection system. CNN belongs to a group of Deep Learning models that are renowned for their remarkable abilities in image identification. Our system, which eliminates the need for expert personal inspection of dermoscopy pictures by using CNN with many layers, ReLU activation function, and Adam optimizer approach, may identify skin lesions.

Keyphrases: CNNs, deep learning, Dermatologist, image processing, Skin Lesion

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ganesh Akkala and Srinu Gunji and Leela Rakesh Koya and Sunil Babu Melingi},
  title = {Skin Cancer Detection Using Deep Learning (CNN)},
  howpublished = {EasyChair Preprint no. 13422},

  year = {EasyChair, 2024}}
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