CNN-based deep learning model for chest x-ray health classification using TensorFlow

• Main Authors: Tobias, Rogelio Ruzcko N.M. I., De Jesus, Luigi Carlo M., Mital, Matt Ervin G., Lauguico, Sandy C., Guillermo, Marielet A., Sybingco, Edwin, Bandala, Argel A., Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2020
• Subjects: Neural networks (Computer scienceMachine learning; Pneumonia—Diagnosis; Biomedical; Electrical and Computer Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2363; https://animorepository.dlsu.edu.ph/context/faculty_research/article/3362/type/native/viewcontent
• Institution: De La Salle University

Incorrect diagnosis is still apparent especially in respiratory diseases. There is truly a need to extend the study with correct diagnosis as most of lung diseases affect children. Over-diagnosis is also a problem that is necessary to address. As an aid to health diagnostics and health professionals, this study constructed a low-cost diagnostic tool that classifies a chest x-ray image if it is under the normal or pneumonia category. Training, validation and cross-entropy were done by using MobileNetV2 as a pre-trained model and served as the general convolutional neural network system. Results yielded high accuracy based on percentage accuracy. Further validation is incorporated by showing the confusion matrix of the system. © 2020 IEEE.