Deep Learning for the classification of pulmonary tissue blocks in High-Resolution Computed Tomography images
Abstract
Interstitial lung disease (ILD) involves several imaging patterns that are observed in high resolution computed tomography (HRCT). The automated tissue characterization is an essential component of a computer aided diagnostic (CAD) system for ILD research. Deep convolutional neural networks learns features directly from training data rather than extracting them manually, avoiding the need for feature extrator optimization. In this context, the present work investigate about a classification method using a deep learning program, in order to improve the performance of CAD systems for the diagnosis of ILDs. Preliminary results achieved a general recognition rate of 74.9 % in the classification of the five classes of ILD refered in this study.
Keywords:
High-resolution computed tomography (HRCT), Interstitial Lung Diseases (ILDs), Tissue classification, Neural networks, Deep-learning
References
A. Depeursinge, A. Vargas, A. P. e. a. (2011). Building a reference multimedia database for interstitial lung diseases. Computerized Medical Imaging and Graphics.
Bagesteiro, L. D. (2015). Classificação de Padrões Radiológicos por Blocos em Imagens não Segmentadas de Tomografia Computadorizada. Universidade Federal do Paraná.
Gao, M. and et al. (2015). Holistic Classification of CT Attenuation Patterns for Interstitial Lung Diseases via Deep Convolutional Neural Networks. Workshop on Deep Learning in Medical Image Analysis (MICCAI).
Hafemann, L. G., Oliveira, L. S., and Cavalin, P. R. (2014). An analysis of deep neural networks for texture classification.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances In Neural Information Processing Systems, pages 1–9.
Lecun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(1):436–444.
Bagesteiro, L. D. (2015). Classificação de Padrões Radiológicos por Blocos em Imagens não Segmentadas de Tomografia Computadorizada. Universidade Federal do Paraná.
Gao, M. and et al. (2015). Holistic Classification of CT Attenuation Patterns for Interstitial Lung Diseases via Deep Convolutional Neural Networks. Workshop on Deep Learning in Medical Image Analysis (MICCAI).
Hafemann, L. G., Oliveira, L. S., and Cavalin, P. R. (2014). An analysis of deep neural networks for texture classification.
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. Advances In Neural Information Processing Systems, pages 1–9.
Lecun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. Nature, 521(1):436–444.
Published
2017-07-02
How to Cite
DARTORA, Aline; DE OLIVEIRA, Lucas Ferrari.
Deep Learning for the classification of pulmonary tissue blocks in High-Resolution Computed Tomography images. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 17. , 2017, São Paulo.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2017
.
p. 1804-1807.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2017.3707.
