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A web-based computer aided diagnosis system on liver disease

Computer aided diagnosis (CAD) system has been proven to be useful in clinical routine. However, different kinds of software installed in different machine limits the widely usage of CAD by doctors. We transfer our previous CAD system on liver disease into a web based program to enable users to diagnose potential hepatic abnormalities through internet, by using XOJO platform which is easy to make web application under BASIC programming language and provide virtual server function when in running mode. There methods for tumor classification are investigated on web programming: GLCM-ANN, Restricted Boltzmann Machine (RBM) w/o edge computing. The result shows that deep learning has better performance to conventional ANN, but the large weight matrix is a big burden of web response speed without edge computing. Our CAD system can be easily open on different OS and any location with network connection. Such convenience makes diagnosis less time consuming while significantly collecting datasets via internet from different hospitals or even patient him/herself.