Abstract
The availability of pulmonary nodules in CT scan image of lung does not completely specify cancer. The noise in an image and morphology of nodules, like shape and size has an implicit and complex association with cancer, and thus, a careful analysis should be mandatory on every suspected nodules and the combination of information of every nodule. In this paper, we introduce a “denoising first” two-path convolutional neural network (DFD-Net) to address this complexity. The introduced model is composed of denoising and detection part in an end to end manner. First, a residual learning denoising model (DR-Net) is employed to remove noise during the preprocessing stage. Then, a two-path convolutional neural network which takes the denoised image by DR-Net as an input to detect lung cancer is employed. The two paths focus on the joint integration of local and global features. To this end, each path employs different receptive field size which aids to model local and global dependencies. To further polish our model performance, in different way from the conventional feature concatenation approaches which directly concatenate two sets of features from different CNN layers, we introduce discriminant correlation analysis to concatenate more representative features. Finally, we also propose a retraining technique that allows us to overcome difficulties associated to the image labels imbalance. We found that this type of model easily first reduce noise in an image, balances the receptive field size effect, affords more representative features, and easily adaptable to the inconsistency among nodule shape and size. Our intensive experimental results achieved competitive results.
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Acknowledgements
This work was partially funded by the national Key research and development program of China (2018YFC0806802 and 2018YFC0832105) and Bule Hora University of Ethiopia. We would like to acknowledge the editors and the anonymous reviewers whose important comments and suggestions led to greatly improved the manuscript.
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Worku J. Sori received the BEd degrees in Mathematics from Mada Walabu University, Ethiopia in 2009, the Master degree in Applied Mathematics from Addis Ababa University, Ethiopia in 2011 and PhD degree in computer science and technology from Harbin Institute of Technology, China in 2019. He is now an assistant professor at the School of Electrical Engineering and Computing, department of Computer science and Engineering, at Adama Science and Technology University (ASTU), Ethiopia. His research interests include medical data processing, pattern recognition, image and video processing, and large data compression.
Jiang Feng received the BS, MS, and PhD degrees in computer science from Harbin Institute of Technology (HIT), China in 2001, 2003, and 2008, respectively. He is now a full professor in the Department of Computer Science, Harbin Institute of Technology and a visiting scholar in the School of Electrical Engineering, Princeton University. His research interests include computer vision, pattern recognition and image and video processing.
Arero W. Godana received the BSc degree from Bule Hora University, Ethiopia, and MSc degrees in computer science from Harbin Institute of Technology, China in 2016 and 2019, respectively. He is now a Lecturer at Arsi University, Ethiopia. His research interests include Satellite image processing, pattern recognition and image and video processing.
Shaohui Liu received the BS. MS. and PhD degrees in computer science from Harbin Institute of Technology (HIT), China in 2000, 2002, and 2007, respectively. He is now an Associated Professor in the Department of Computer Science, Harbin Institute of Technology, China. His research interests include data compression, pattern recognition and image and video processing.
Demissie J. Gelmecha received the BSc degree in physics from Haramaya University, Ethiopia in 2006, the MSc degree in physical electronics engineering from Central China Normal University, Institute of Technology, China in 2011 and PhD degree in optical Engineering from Harbin Institute of Technology, China in 2018. Currently, he is an Assistant Professor of Optical Engineering at the Department of Electronics and Communication Engineering, School of Electrical Engineering and Computing at Adama Science and Technology University (ASTU), Ethiopia. He is also serving the University as Dean of Academic Staff Affairs. His current research interests include fiber-optic communications, non-linear chiral fibers for optical communications and new optical devices, and full-duplex communication systems.
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Sori, W.J., Feng, J., Godana, A.W. et al. DFD-Net: lung cancer detection from denoised CT scan image using deep learning. Front. Comput. Sci. 15, 152701 (2021). https://doi.org/10.1007/s11704-020-9050-z
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DOI: https://doi.org/10.1007/s11704-020-9050-z