CNN-based Deep Learning Model for Chest X-ray Health Classification Using TensorFlow

被引：13

作者：

Tobias, Rogelio Ruzcko ^{[1
]}

De Jesus, Luigi Carlo M. ^{[1
]}

Mital, Matt Ervin G. ^{[2
]}

Lauguico, Sandy C. ^{[2
]}

Guillermo, Marielet A. ^{[1
]}

Sybingco, Edwin ^{[2
]}

Bandala, Argel A. ^{[2
]}

Dadios, Elmer P. ^{[2
]}

机构：

[1] Asia Pacific Coll, Sch Engn, Makati, Philippines

[2] De La Salle Univ, Dept Elect Engn, Manila, Philippines

来源：

2020 RIVF INTERNATIONAL CONFERENCE ON COMPUTING & COMMUNICATION TECHNOLOGIES (RIVF 2020) | 2020年

关键词：

bottleneck; convolutional neural network; frozen graph; MobileNetV2; pneumonia; TensorFlow;

D O I：

10.1109/rivf48685.2020.9140733

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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.

引用

页码：192 / 197

页数：6

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