Artificial Intelligence Techniques applied to Biomedical Patterns

Pattern recognition is the research area of Artificial Intelligence that studies the operation and design of systems that recognize patterns in the data. Important application areas are image analysis, character recognition, fingerprint classification, speech analysis, DNA sequence identification, man and machine diagnostics, person identification and industrial inspection. The interest in improving the classification systems of data analysis is independent from the context of applications. In fact, in many studies it is often the case to have to recognize and to distinguish groups of various objects, which requires the need for valid instruments capable to perform this task. The objective of this article is to show several methodologies of Artificial Intelligence for data classification applied to biomedical patterns. In particular, this work deals with the realization of a Computer-Aided Detection system (CADe) that is able to assist the radiologist in identifying types of mammary tumor lesions. As an additional biomedical application of the classification systems, we present a study conducted on blood samples which shows how these methods may help to distinguish between carriers of Thalassemia (or Mediterranean Anaemia) and healthy subjects.

Authors:

• Giovanni Luca Masala

Keywords:

• Computer Aided Detection
• mammary tumor
• pattern recognition
• thalassemia.

References:

[1] O. Duda, P. E. Hart, D. G. Stark, "Pattern Classification", second
edition, A Wiley-Interscience Publication John Wiley & Sons, 2001.
[2] S. Haykin "Neural Networks - A comprehensive foundation", second
edition, Prentice Hall, 1999.
[3] V. N. Vapnik. "Statistical Learning Theory. Wiley", New York , 1998.
[4] M. Pontil, A. Verri "Properties of Support Vector Machines", Neural
Computation, Vol. 10, pp 955-974, 1998.
[5] Bottigli et al, Search of Microcalcification clusters with the CALMA
CAD station. The International Society for Optical Engineering (SPIE)
4684: 1301-1310, 2002
[6] F. Fauci et al, Mammogram Segmentation by Contour Searching and
Massive Lesion Classification with Neural Network, Proc. IEEE
Medical Imaging Conference, October 16-22 2004, Rome, Italy; M2-
373/1-5, 2004.
[7] G. Masala, B. Golosio, D. Cascio, F. Fauci, S. Tangaro, M. Quarta, S. C
Cheran, E. L. Torres, "Classifiers trained on dissimilarity representation
of medical pattern: a comparative study" on Nuovo Cimento C, Vol
028, Issue 06, pp 905-912 , 2005.
[8] E. Pekalska, R.P.W. Duin, R.P.W. and P.Paclik, "Prototype Selection for
Dissimilarity-based Classifiers", Pattern Recognition, vol. 39, no. 2, pp.
189-208, February 2006.
[9] Hanley JA, McNeil B, A method of comparing the areas under receiver
operating characteristic curves derived from the same cases, Radiology:
148; 839-843, 1983.
[10] S.R. Amendolia, G. Cossu, M. L. Ganadu, B. Golosio, G.L. Masala,
G.M. Mura "A Comparative study of K-Nearest Neighbour, Support
Vector Machine and Multi-Layer Perceptron for Thalassemia Screening"
on "Chemometrics and intelligent laboratory system" ;69:13-20, 2003.
[11] S.R Amendolia , A. Brunetti, P.Carta, G. Cossu, M.L. Ganadu, B.
Golosio, G.M. Mura, M.G. Pirastru, A Real-Time Classification System
of Thalassemic Pathologies Based on Artificial Neural Networks
Medical Decision Making; 22:18-26, 2002.