Classification of radiological patterns of tuberculosis with a Convolutional neural network in x-ray images
Trueba Espinosa, Adrian (Universidad Autónoma del Estado de México)
Sanchez -Arrazola, Jessica (Universidad Autónoma del Estado de México)
Cervantes, Jair (Universidad Autónoma del Estado de México)
Garcia-Lamont, Farid (Universidad Autónoma del Estado de México)
Ruiz Castilla, José Sergio (Universidad Autónoma del Estado de México)
Kantipudi, Karthik (National Institutes of Health (US))

Date:	2024
Abstract:	In this paper we propose the classification of radiological patterns with the presence of tuberculosis in X-ray images, it was observed that two to six patterns (consolidation, fibrosis, opacity, opacity, pleural, nodules and cavitations) are present in the radiographs of the patients. It is important to mention that species specialists consider the type of TB pattern in order to provide appropriate treatment. It should be noted that not all medical centres have specialists who can immediately interpret radiological patterns. Considering the above, the aim is to classify patterns by means of a convolutional neural network to help make a more accurate diagnosis on X-rays, so that doctors can recommend immediate treatment and thus avoid infecting more people. For the classification of tuberculosis patterns, a proprietary convolutional neural network (CNN) was proposed and compared against the VGG16, InceptionV3 and ResNet-50 architectures, which were selected based on the results of other radiograph classification research [1]-[3] . The results obtained for the Macro-averange AUC-SVM metric for the proposed architecture and InceptionV3 were 0. 80, and for VGG16 it was 0. 75, and for the ResNet-50 network it was 0. 79. The proposed architecture has better classification results, as does InceptionV3.
Rights:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades.
Language:	Anglès
Document:	Article ; recerca ; Versió publicada
Subject:	Tuberculosis patterns ; Convolutional neural networks ; Chest x-rays
Published in:	ELCVIA. Electronic letters on computer vision and image analysis, Vol. 23 Núm. 1 (2024) , p. 47-59 (Regular Issue) , ISSN 1577-5097

Adreça original: https://elcvia.cvc.uab.cat/article/view/1561
Adreça alternativa: https://raco.cat/index.php/ELCVIA/article/view/980000001025
DOI: 10.5565/rev/elcvia.1561

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