Balanced Medical Image Classification with Transfer Learning and Convolutional Neural Networks
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Fecha
2022
Profesor/a Guía
Facultad/escuela
Idioma
en
Título de la revista
ISSN de la revista
Título del volumen
Editor
MDPI
Nombre de Curso
Licencia CC
Attribution 4.0 International (CC BY 4.0)
Licencia CC
https://creativecommons.org/licenses/by/4.0/
Resumen
This paper aims to propose a tool for image classification in medical diagnosis decision support, in a context where computational power is limited and then specific, high-speed computing infrastructures cannot be used (mainly for economic and energy consuming reasons). The proposed method combines a deep neural networks algorithm with medical imaging procedures and is implemented to allow an efficient use on affordable hardware. The convolutional neural network (CNN) procedure used VGG16 as its base architecture, using the transfer learning technique with the parameters obtained in the ImageNet competition. Two convolutional blocks and one dense block were added to this architecture. The tool was developed and calibrated on the basis of five common lung diseases using 5430 images from two public datasets and the transfer learning technique. The holdout ratios of 90% and 10% for training and testing, respectively, were obtained, and the regularization tools were dropout, early stopping, and Lasso regularization (L2). An accuracy (ACC) of 56% and an area under the receiver-operating characteristic curve (ROC—AUC) of 50% were reached in testing, which are suitable for decision support in a resource-constrained environment.
Notas
Indexación Scopus
Palabras clave
Chest X-rays, Computer vision, Convolutional neural nets, Deep learning, Image classification, Medical imaging, Problem solving
Citación
Axioms Volume 11, Issue 3 March 2022 Article number 115
DOI
10.3390/axioms11030115