Data Mining Techniques in Computer-Aided Diagnosis: Non-Invasive Cancer Detection
Diagnosis can be achieved by building a model of a
certain organ under surveillance and comparing it with the real time
physiological measurements taken from the patient. This paper deals
with the presentation of the benefits of using Data Mining techniques
in the computer-aided diagnosis (CAD), focusing on the cancer
detection, in order to help doctors to make optimal decisions quickly
and accurately. In the field of the noninvasive diagnosis techniques,
the endoscopic ultrasound elastography (EUSE) is a recent elasticity
imaging technique, allowing characterizing the difference between
malignant and benign tumors. Digitalizing and summarizing the main
EUSE sample movies features in a vector form concern with the use
of the exploratory data analysis (EDA). Neural networks are then
trained on the corresponding EUSE sample movies vector input in
such a way that these intelligent systems are able to offer a very
precise and objective diagnosis, discriminating between benign and
malignant tumors. A concrete application of these Data Mining
techniques illustrates the suitability and the reliability of this
methodology in CAD.
[1] M. Giovannini, L. Hookey, E. Bories et al., "Endoscopic ultrasound
elastography: the first step towards virtual biopsy? Preliminary results in
49 patients," Endoscopy, vol. 38, pp. 344-348, 2006.
[2] W. Rasband, "ImageJ: image processing and analysis in JAVA",
National Institutes of Health (Available: http://rsb.info.nih.gov/ij/).
[3] A. Saftoiu., P. Vilmann, H. Hassan, and F. Gorunescu, "Analysis of
endoscopic ultrasound elastography used for characterization and
differentiation of benign and malignant lymph nodes", Ultraschall in der
Medizin (European Journal of Ultrasound), vol 27, no. 6, pp. 535-542,
2006.
[4] A. Saftoiu, C. Popescu, S. Cazacu, D. Dumitrescu, C.V. Georgescu, M.
Popescu, T. Ciurea, and F. Gorunescu, "Power Doppler Endoscopic
Ultrasound for the Differential Diagnosis between Pancreatic Cancer and
Pseudotumoral Chronic Pancreatitis", Journal of Ultrasound in
Medicine, vol. 25, no. 3, pp. 363-372, 2006.
[5] A. Saftoiu, P. Vilmann, T. Ciurea, G.L. Popescu, A. Iordache, H.
Hassan, F. Gorunescu, S. Iordache, "Dynamic analysis of endoscopic
ultrasound (EUS) elastography used for the differentiation of benign and
malignant lymph nodes", Gastrointestinal Endoscopy, vol. 66, no. 2, pp.
291-300, 2007.
[6] S. Haykin, Neural Networks. Prentice Hall International, 1999.
[1] M. Giovannini, L. Hookey, E. Bories et al., "Endoscopic ultrasound
elastography: the first step towards virtual biopsy? Preliminary results in
49 patients," Endoscopy, vol. 38, pp. 344-348, 2006.
[2] W. Rasband, "ImageJ: image processing and analysis in JAVA",
National Institutes of Health (Available: http://rsb.info.nih.gov/ij/).
[3] A. Saftoiu., P. Vilmann, H. Hassan, and F. Gorunescu, "Analysis of
endoscopic ultrasound elastography used for characterization and
differentiation of benign and malignant lymph nodes", Ultraschall in der
Medizin (European Journal of Ultrasound), vol 27, no. 6, pp. 535-542,
2006.
[4] A. Saftoiu, C. Popescu, S. Cazacu, D. Dumitrescu, C.V. Georgescu, M.
Popescu, T. Ciurea, and F. Gorunescu, "Power Doppler Endoscopic
Ultrasound for the Differential Diagnosis between Pancreatic Cancer and
Pseudotumoral Chronic Pancreatitis", Journal of Ultrasound in
Medicine, vol. 25, no. 3, pp. 363-372, 2006.
[5] A. Saftoiu, P. Vilmann, T. Ciurea, G.L. Popescu, A. Iordache, H.
Hassan, F. Gorunescu, S. Iordache, "Dynamic analysis of endoscopic
ultrasound (EUS) elastography used for the differentiation of benign and
malignant lymph nodes", Gastrointestinal Endoscopy, vol. 66, no. 2, pp.
291-300, 2007.
[6] S. Haykin, Neural Networks. Prentice Hall International, 1999.
@article{"International Journal of Medical, Medicine and Health Sciences:53829", author = "Florin Gorunescu", title = "Data Mining Techniques in Computer-Aided Diagnosis: Non-Invasive Cancer Detection", abstract = "Diagnosis can be achieved by building a model of a
certain organ under surveillance and comparing it with the real time
physiological measurements taken from the patient. This paper deals
with the presentation of the benefits of using Data Mining techniques
in the computer-aided diagnosis (CAD), focusing on the cancer
detection, in order to help doctors to make optimal decisions quickly
and accurately. In the field of the noninvasive diagnosis techniques,
the endoscopic ultrasound elastography (EUSE) is a recent elasticity
imaging technique, allowing characterizing the difference between
malignant and benign tumors. Digitalizing and summarizing the main
EUSE sample movies features in a vector form concern with the use
of the exploratory data analysis (EDA). Neural networks are then
trained on the corresponding EUSE sample movies vector input in
such a way that these intelligent systems are able to offer a very
precise and objective diagnosis, discriminating between benign and
malignant tumors. A concrete application of these Data Mining
techniques illustrates the suitability and the reliability of this
methodology in CAD.", keywords = "Endoscopic ultrasound elastography, exploratorydata analysis, neural networks, non-invasive cancer detection.", volume = "1", number = "10", pages = "532-4", }