TX-CNN: Detecting tuberculosis in chest X-ray images using convolutional neural network

Chang Liu, Yu Cao, Marlon Alcantara, Benyuan Liu, Maria Brunette, Jesus Peinado, Walter Curioso

Producción científica: Libro o Capítulo del libro Contribución a la conferenciarevisión exhaustiva

109 Citas (Scopus)

Resumen

In Low and Middle-Income Countries (LMICs), efforts to eliminate the Tuberculosis (TB) epidemic are challenged by the persistent social inequalities in health, the limited number of local healthcare professionals, and the weak healthcare infrastructure found in resource-poor settings. The modern development of computer techniques has accelerated the TB diagnosis process. In this paper, we propose a novel method using Convolutional Neural Network(CNN) to deal with unbalanced, less-category X-ray images. Our method improves the accuracy for classifying multiple TB manifestations by a large margin. We explore the effectiveness and efficiency of shuffle sampling with cross-validation in training the network and find its outstanding effect in medical images classification. We achieve an 85.68% classification accuracy in a large TB image dataset, surpassing any state-of-art classification accuracy in this area. Our methods and results show a promising path for more accurate and faster TB diagnosis in LMICs healthcare facilities.

Idioma originalInglés estadounidense
Título de la publicación alojada2017 IEEE International Conference on Image Processing, ICIP 2017 - Proceedings
EditorialIEEE Computer Society
Páginas2314-2318
-5
ISBN (versión digital)9781509021758
DOI
EstadoIndizado - 2 jul. 2017
Publicado de forma externa
Evento24th IEEE International Conference on Image Processing, ICIP 2017 - Beijing, China
Duración: 17 set. 201720 set. 2017

Serie de la publicación

NombreProceedings - International Conference on Image Processing, ICIP
Volumen2017-September
ISSN (versión impresa)1522-4880

Conferencia

Conferencia24th IEEE International Conference on Image Processing, ICIP 2017
País/TerritorioChina
CiudadBeijing
Período17/09/1720/09/17

Nota bibliográfica

Publisher Copyright:
© 2017 IEEE.

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