The skin cancer classification using deep convolutional neural network

By Ulzii-Orshikh Dorj, Keun Kwang Lee, Jae-Young Choi, Malrey Lee. Multimedia tools and applications, 2018

368 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Epidemiology, Oncology

View Paper: https://www.semanticscholar.org/paper/75912888110d0f276bb13631168ced055e8f5d1a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ceren Kaya
2. Ali Narin
3. Ziynet Pamuk

Citations in the corpus

• Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, background): “As a result of these analyzes, detection and diagnosis of diseases such as diabetes mellitus, brain tumor, skin cancer and breast cancer are provided with convenience [36-41].”
• Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review (2018, background): “Works in which the origin of the performance was not plausible are not considered in this work, for example, in Carcagnì et al [15] or Dorj et al [16].”
• Skin Cancer Detection: A Review Using Deep Learning Techniques (2021, background): “[36] BCC/SCC/melanoma/AK SVM with deep CNN 3753 dermoscopic images Pertained to deep CNN and AlexNet for features extraction Accuracy (SCC: 95.”