  This study focuses on the application of a specific subfield of artificial
intelligence referred to as computer vision in the analysis of 2-dimensional
lung x-ray images for the assisted medical diagnosis of ordinary pneumonia.
  A convolutional neural network algorithm was implemented in a Python-coded,
Flask-based web application that can analyze x-ray images for the detection of
ordinary pneumonia. Since convolutional neural network algorithms rely on
machine learning for the identification and detection of patterns, a technique
referred to as transfer learning was implemented to train the neural network in
the identification and detection of patterns within the dataset. Open-source
lung x-ray images were used as training data to create a knowledge base that
served as the core element of the web application and the experimental design
employed a 5-Trial Confirmatory Test for the validation of the web application.
  The results of the 5-Trial Confirmatory Test show the calculation of
Diagnostic Precision Percentage per Trial, General Diagnostic Precision
Percentage, and General Diagnostic Error Percentage while the Confusion Matrix
further shows the relationship between the label and the corresponding
diagnosis result of the web application on each test images.
  The developed web application can be used by medical practitioners in
A.I.-assisted diagnosis of ordinary pneumonia, and by researchers in the fields
of computer science and bioinformatics.
