Diabetes Detection Using Carbon Nanomaterial Coated QCM Gas Sensors and a Convolutional Neural Network through Urine Sample

Diabetes is a disease that the entire world is afraid of. Early detection measures are required in this instance. Diabetes diagnosis by blood samples has the disadvantage of being uncomfortable. This study uses a urine sample to estimate a person's risk of developing diabetes. The electronic nose is a system that has the potential to recognize diabetes in this way. This method consists of quartz crystal microbalance gas sensors coated with carbon nanomaterials, including single-walled carbon nanotubes, double-walled carbon nanotubes, multi-walled carbon nanotubes, and graphene oxide. A reciprocal counter implemented in a field programmable gate array (FPGA) device is used to measure the frequency shift on the sensor. The convolutional neural network technique is used to detect diabetes. The results of the experiments suggest that this system can distinguish between healthy and diabetic people with an accuracy of 91%.