Skin Cancer Detection using Deep Neural Networks2018 Feb 08
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 defeconvoat. 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.
Conclusion: We have trained our model using Vgg16, Inception and ResNet50 neural network architecture. In training, we have provided 2 categories of images one with Malignant (MelanomaSkin cancer) tumors and other with benign tumors. After training, we tested our model with random images of tumor and an accuracy of 83.86%-86.02% was recorded in classifying that it is malignant or benign. By using neural network our model can classify Malignant(cancerous) and benign(non-cancerous) tumors with an accuracy of 86.02%. Since cancer, if detected early can be cured completely. This technology can be used to detect cancer when a tumor is developed at early stage and precautions can be taken accordingly.