Centralized pap test diagnosis with artificial neural network and internet of things

In this work, we propose a utilization of a server-client system model that using the Internet of Things (IoT) technology to process Pap smear imaging data derived from high-resolution microscopes and to classify the those images by employing the Artificial Neural Network (ANN) learning algorithm on the server. The IoT can enable those microscopes to communicate with one another while the ANN enables a new method of imaging classification with high accuracy. We utilize 917 high-resolution images as an input for our proposed method. The method achieves a root mean square error of 0.8834 and correlation coefficient of 0.6643.