Skin Cancer Detection using Deep Neural Networks

Rajat Sharma, Swapnil Pote

Abstract


Cancer is the most dangerous and stubborn disease known to mankind. It accounts for the most deaths caused by any disease. However, if detected early this medical condition is not very difficult to defeat. Tumors which are cancerous grow very rapidly and spread into different parts of the body and this process continues until that tumor spreads in the entire body and ultimately our organs stop functioning. If any tumor is developed in any part of our body it requires immediate medical attention to verify that the tumor is malignant(cancerous) or Benign(non-cancerous). Until now if any tumor has to be tested for malignancy a sample of tumor should be extracted out and then tested in the laboratory. But using the computational logic of Deep Neural Networks we can predict that the tumor is malignant or Benign by only a photograph of that tumor. If cancer is detected in early stage chances are very high that it can be cured completely. In this work, we detect Melanoma(Skin cancer) in tumors by processing images of those tumors.


Full Text:

PDF

References


Karen Simonyan, Andrew Zisserman: Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv:1409.1556

Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, Andrew Rabinovich: Going Deeper with Convolutions.

arXiv:1409.4842

Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun: Deep Residual Learning for Image Recognition. arXiv:1512.03385

A. Krizhevsky, I. Sutskever, and G. Hinton. ImageNet classification with deep convolutional neural networks. In NIPS, 2012

Leslie N. Smith: Cyclical Learning Rates for Training Neural Networks. arXiv:1506.0118

Matt Berseth: ISIC 2017 - Skin Lesion Analysis Towards Melanoma Detection. arXiv:1703.00523


Refbacks

  • There are currently no refbacks.