This article describes existing methods of H. pylori diseases diagnosis, procedures of interpreting acquired results and the diagnosis method with the use of semi-conductor catalytic gas sensors combined into the system.
Gas sensors have cross-sensitivity to various gases in addition for which they are designed. The use of several sensors allows to reduce the influence of mixed gases. This method is especially useful within medical entities, where air inside can contain alcohol or chloramine vapors. Sensors are selected in the way to overlap main sensor´s cross-sensitivity zone to the maximum extent possible. This is how mixed gases´ influence on the main sensor is compensated. The proposed system uses methods of artificial neural network technology, which allows to enhance system´s stability in changing gas mixture. Due to microcontroller driven calculations, the system can automatically provide data processing. The proposed system can reduce the influence of factors that contribute uncertainty to the measurement result. These results can be transmitted to PC, which one can use to create electronic database or to hold case history.
Research on Pepper Disease Diagnosis Model Based on Genetic Neural network
