Android application for chest x-ray health classification from a CNN deep learning TensorFlow model

• Main Authors: Tobias, Rogelio Ruzcko, De Jesus, Luigi Carlo, Mital, Matt Ervin, Lauguico, Sandy C., Guillermo, Marielet, Sybingco, Edwin, Bandala, Argel A., Dadios, Elmer Jose P.
• Format: text
• Published: Animo Repository 2020
• Subjects: Diagnosis, Radioscopic; Pneumonia—Diagnosis; Application software; Neural networks (Computer science); Image processing; Electrical and Computer Engineering; Electrical and Electronics; Systems and Communications
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1713; https://animorepository.dlsu.edu.ph/context/faculty_research/article/2712/type/native/viewcontent
• Institution: De La Salle University

© 2020 IEEE. One of the problems in the medical field is incorrect diagnosis, particularly over-diagnosis and under diagnosis. One of the illnesses that is currently researched upon is pneumonia. Several methodologies are employed to further validate this diagnosis. Often, to achieve the goal, medical experts rely on an x-ray image. In this study, the basis is still x-ray images with the incorporation of image processing and machine learning. MobileNetV2 is utilized as the convolution neural network model. The produced frozen graph is injected to Android Studio to produce an android mobile application which will serve as a diagnostic tool. The mobile application has high accuracy and considered reliable because of testing and validation results. This study generally aims to provide a reliable low-cost aid for medical professionals in diagnosing pneumonia.