项目作者： nps6-uwf
项目描述 ：
In this paper I introduce a new oral lesion database that I created by scraping Images from research papers and dentists online. Next, I showcase a novel method for generating NxN pixel samples from human lips by isolating the labial fissure via image segmentation induced via the Kmean algorithm. I experiment with a variety of convolutional neural network architectures including: a simple network, AlexNet, VGG, and ResNet in an effort to correctly classify oral tissue samples of: tongue, lip, HSV-1, and squamous cell carcinoma. First I consider the dataset as a whole and attempt multi class classification, then I carry out multiple binary classification experiments including: lesion versus non-lesion, herpes versus squamous cell carcinoma, and tongue versus lip. Finally I implement a novel method that attempts to perform the same multi class classification that I started with. This method involves an ensemble of binary ResNet networks. I conclude with an overview of lots of future research that can be done on this problem.
高级语言： Python
项目地址: git://github.com/nps6-uwf/CNN-for-Oral-Lesion-Classification.git
