Skin Cancer Recognition using Deep Residual Network
This addresses a healthcare gap for patients in remote areas, but it is incremental as it applies an existing method to a specific medical dataset.
The paper tackled skin cancer detection from images to improve healthcare access in remote areas, achieving 77% accuracy on the ISIC-2017 challenge using a deep residual network.
The advances in technology have enabled people to access internet from every part of the world. But to date, access to healthcare in remote areas is sparse. This proposed solution aims to bridge the gap between specialist doctors and patients. This prototype will be able to detect skin cancer from an image captured by the phone or any other camera. The network is deployed on cloud server-side processing for an even more accurate result. The Deep Residual learning model has been used for predicting the probability of cancer for server side The ResNet has three parametric layers. Each layer has Convolutional Neural Network, Batch Normalization, Maxpool and ReLU. Currently the model achieves an accuracy of 77% on the ISIC - 2017 challenge.