TY - GEN
T1 - Evaluation of deep feedforward neural networks for classification of diffuse lung diseases
AU - Cardoso, Isadora
AU - Almeida, Eliana
AU - Allende-Cid, Héctor
AU - Frery, Alejandro C.
AU - Rangayyan, Rangaraj M.
AU - Azevedo-Marques, Paulo M.
AU - Ramos, Heitor S.
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - Diffuse Lung Diseases (DLDs) are a challenge for physicians due their wide variety. Computer-Aided Diagnosis (CAD) are systems able to help physicians in their diagnoses combining information provided by experts with Machine Learning (ML) methods. Among ML techniques, Deep Learning has recently established itself as one of the preferred methods with state-of-the-art performance in several fields. In this paper, we analyze the discriminatory power of Deep Feedforward Neural Networks (DFNN) when applied to DLDs. We classify six radiographic patterns related with DLDs: pulmonary consolidation, emphysematous areas, septal thickening, honeycomb, ground-glass opacities, and normal lung tissues. We analyze DFNN and other ML methods to compare their performance. The obtained results show the high performance obtained by DFNN method, with an overall accuracy of 99.60%, about 10% higher than the other studied ML methods.
AB - Diffuse Lung Diseases (DLDs) are a challenge for physicians due their wide variety. Computer-Aided Diagnosis (CAD) are systems able to help physicians in their diagnoses combining information provided by experts with Machine Learning (ML) methods. Among ML techniques, Deep Learning has recently established itself as one of the preferred methods with state-of-the-art performance in several fields. In this paper, we analyze the discriminatory power of Deep Feedforward Neural Networks (DFNN) when applied to DLDs. We classify six radiographic patterns related with DLDs: pulmonary consolidation, emphysematous areas, septal thickening, honeycomb, ground-glass opacities, and normal lung tissues. We analyze DFNN and other ML methods to compare their performance. The obtained results show the high performance obtained by DFNN method, with an overall accuracy of 99.60%, about 10% higher than the other studied ML methods.
KW - Computer-aided diagnosis
KW - Deep feedforward neural network
KW - Deep learning
KW - Diffuse lung diseases
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85042208941&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-75193-1_19
DO - 10.1007/978-3-319-75193-1_19
M3 - Conference contribution
AN - SCOPUS:85042208941
SN - 9783319751924
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 152
EP - 159
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 22nd Iberoamerican Congress, CIARP 2017, Proceedings
A2 - Velastin, Sergio
A2 - Mendoza, Marcelo
PB - Springer Verlag
T2 - 22nd Iberoamerican Congress on Pattern Recognition, CIARP 2017
Y2 - 7 November 2017 through 10 November 2017
ER -