Pixel-based Machine Learning in Computer-Aided Diagnosis of Lung and Colon Cancer

  • Kenji SuzukiEmail author
Part of the Intelligent Systems Reference Library book series (ISRL, volume 56)


Computer-aided diagnosis (CAD) for detection of lesions in medical images has been an active area of research. Machine learning plays an essential role in CAD, because representing lesions and organs requires a complex model that has a number of parameters to determine; thus, medical pattern recognition essentially requires “learning from examples” to determine the parameters of the model. Machine learning has been used to classify lesions into certain classes (e.g., abnormal or normal, lesions or non-lesions, and malignant or benign) in CAD. Recently, as available computational power increased dramatically, pixel/voxel-based machine learning (PML) has emerged in medical image processing/analysis, which uses pixel/voxel values in local regions (or patches) in images instead of features calculated from segmented regions as input information; thus, feature calculation or segmentation is not required. Because PML can avoid errors caused by inaccurate feature calculation and segmentation, the performance of PML can potentially be higher than that of common classifiers. In this chapter, MTANNs (a class of PML) in CAD schemes for detection of lung nodules in CT and for detection of polyps in CTC are presented.


Artificial Neural Network Support Vector Regression Lung Nodule Nodule Candidate Lung Cancer Screening 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work would not have been possible without the help of countless individuals. The author acknowledges the invaluable assistance of all colleagues and support staff. The author is grateful to all members in the Suzuki Laboratory in the Department of Radiology at the University of Chicago, especially Ivan Sheu, Mark L Epstein, Jianwu Xu, and Sheng Chen, for their contributions to the studies, to colleagues and collaborators, especially Abraham H Dachman, Heber MacMahon, Kunio Doi, Samuel G Armato III, Feng Li, Shusuke Sone, Hiroyuki Abe, Qiang Li, Junji Shiraishi, Don C Rockey, Hiroyuki Yoshida, and Janne Nappi for their valuable suggestions, and to Ms. E. F. Lanzl for improving the chapter. The author is also grateful to his wife, Harumi Suzuki, for her assistance with the chapter and studies, and his daughters, Mineru Suzuki and Juno Suzuki, for cheering him up. This work was partly supported by Grant Number R01CA120549 from the National Cancer Institute/National Institutes of Health and by NIH S10 RR021039 and P30 CA14599.


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© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Department of Radiology, Division of Biological SciencesThe University of ChicagoChicagoUSA

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