Dr. Daniel W. McKee Profile

Dr. Daniel W. McKee

Assistant Professor at Mansfield Univ

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Publications (7)

Proceedings Article | 15 March 2006 Paper

An adaptive image segmentation process for the classification of lung biopsy images

Daniel McKee, Walker Land, Tatyana Zhukov, Dansheng Song, Wei Qian

Proceedings Volume 6144, 614452 (2006) https://doi.org/10.1117/12.650105

KEYWORDS: Image segmentation, Image processing, Cancer, Tissues, Image classification, Lung, Microscopes, Fuzzy logic, Lung cancer, Feature extraction

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Proceedings Article | 12 May 2004 Paper

Breast cancer classification improvements using a new kernel function with evolutionary-programming-configured support vector machines

Walker Land, Daniel McKee, Frances Anderson, Joseph Lo

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.535864

KEYWORDS: Breast cancer, Mammography, Biopsy, Chemical elements, Cancer, Computer programming, Breast, Image processing, Iterative methods, Medicine

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Proceedings Article | 12 May 2004 Paper

New results in computer-aided diagnosis (CAD) of breast cancer using a recently developed SVM/GRNN Oracle hybrid

Walker Land, Lut Wong, Daniel McKee, Timothy Masters, Frances Anderson, Anurag Raturi, Joseph Lo

Proceedings Volume 5370, (2004) https://doi.org/10.1117/12.533142

KEYWORDS: Breast cancer, Mammography, Computer aided diagnosis and therapy, Neural networks, Error analysis, Dysprosium, Breast, Virtual colonoscopy, Lung cancer, Biopsy

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Proceedings Article | 12 April 2004 Paper

Data fusion of several support-vector-machine breast-cancer diagnostic paradigms using a GRNN oracle

Walker Land, Lut Wong, Dan McKee, Timothy Masters, Frances Anderson, Sapan Sarvaiya

Proceedings Volume 5434, (2004) https://doi.org/10.1117/12.541040

KEYWORDS: Breast cancer, Mammography, Data fusion, Diagnostics, Lung cancer, Tumors, Biopsy, Neural networks

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Proceedings Article | 15 May 2003 Paper

Improving the predictive value of mammography using a specialized evolutionary programming hybrid and fitness functions

Walker Land, Daniel McKee, Joseph Lo, Frances Anderson

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.483550

KEYWORDS: Mammography, Biopsy, Cancer, Computer programming, Breast cancer, Chemical elements, Gold, Breast, Neural networks, Error analysis

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Proceedings Article | 15 May 2003 Paper

Application of support vector machines to breast cancer screening using mammogram and clinical history data

Walker Land, Dan McKee, Roberto Velazquez, Lut Wong, Joseph Lo, Francis Anderson

Proceedings Volume 5032, (2003) https://doi.org/10.1117/12.480235

KEYWORDS: Mammography, Breast cancer, Cancer, Data centers, Neural networks, Iterative methods, Virtual colonoscopy, Source mask optimization, Breast, Data modeling

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The objectives of this paper are to discuss: (1) the development and testing of a new Evolutionary Programming (EP) method to optimally configure Support Vector Machine (SVM) parameters for facilitating the diagnosis of breast cancer; (2) evaluation of EP derived learning machines when the number of BI-RADS and clinical history discriminators are reduced from 16 to 7; (3) establishing system performance for several SVM kernels in addition to the EP/Adaptive Boosting (EP/AB) hybrid using the Digital Database for Screening Mammography, University of South Florida (DDSM USF) and Duke data sets; and (4) obtaining a preliminary evaluation of the measurement of SVM learning machine inter-institutional generalization capability using BI-RADS data. Measuring performance of the SVM designs and EP/AB hybrid against these objectives will provide quantative evidence that the software packages described can generalize to larger patient data sets from different institutions. Most iterative methods currently in use to optimize learning machine parameters are time consuming processes, which sometimes yield sub-optimal values resulting in performance degradation. SVMs are new machine intelligence paradigms, which use the Structural Risk Minimization (SRM) concept to develop learning machines. These learning machines can always be trained to provide global minima, given that the machine parameters are optimally computed. In addition, several system performance studies are described which include EP derived SVM performance as a function of: (a) population and generation size as well as a method for generating initial populations and (b) iteratively derived versus EP derived learning machine parameters. Finally, the authors describe a set of experiments providing preliminary evidence that both the EP/AB hybrid and SVM Computer Aided Diagnostic C++ software packages will work across a large population of patients, based on a data set of approximately 2,500 samples from five different institutions.

Proceedings Article | 3 July 2001 Paper

Application of adaptive boosting to EP-derived multilayer feed-forward neural networks (MLFN) to improve benign/malignant breast cancer classification

Walker Land, Timothy Masters, Joseph Lo, Dan McKee

Proceedings Volume 4322, (2001) https://doi.org/10.1117/12.431058

KEYWORDS: Mammography, Neural networks, Breast cancer, Biopsy, Databases, Computer programming, Machine learning, Algorithm development, Network architectures, Statistical analysis

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Showing 5 of 7 publications

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