CNN-based deep learning model for chest x-ray health classification using TensorFlow
College
Gokongwei College of Engineering
Department/Unit
Electronics And Communications Engg
Document Type
Conference Proceeding
Source Title
Proceedings - 2020 RIVF International Conference on Computing and Communication Technologies, RIVF 2020
Publication Date
10-1-2020
Abstract
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.
html
Digitial Object Identifier (DOI)
10.1109/RIVF48685.2020.9140733
Recommended Citation
Tobias, R. I., De Jesus, L. M., Mital, M. G., Lauguico, S. C., Guillermo, M. A., Sybingco, E., Bandala, A. A., & Dadios, E. P. (2020). CNN-based deep learning model for chest x-ray health classification using TensorFlow. Proceedings - 2020 RIVF International Conference on Computing and Communication Technologies, RIVF 2020 https://doi.org/10.1109/RIVF48685.2020.9140733
Disciplines
Biomedical | Electrical and Computer Engineering
Keywords
Neural networks (Computer scienceMachine learning; Pneumonia—Diagnosis
Upload File
wf_yes
