Yapay Zeka Teknikleri ve Radyolojiye Uygulanması

Moe MC,
Westerlun
d U, Varghese M, Berg-Johnsen J, Svensson M, Langmoen IA. Development of neuronal networks from single stem cells harvested from the adult human brain. Neurosurgery 2005; 56(6):1182-90.
2. Baxt WG. Application of artificial neural networks to clinical medicine. Lancet 1995; 346:1135-8.
3. Ergezer H,
Dikme
n M, Özdemir E. Yapay sinir ağları ve tanıma sistemleri. PiVOLKA 2003; 2(6):14-17.
1.
4
Fırat Tıp Dergisi 2009;14(1): 01-06
4. Zadeh, LA. Biological application of the theory of fuzzy sets and systems on Biocybernetics of the Central Nervous System,
Proc Int Sym 1969; 199-212.
5. Mobley BA, Schechter E, Moore WE, McKee PA, Eichner JE. Neural network predictions of significant coronary artery stenosis in men. Artif Intell Med 2005; 34(2):151-61.
6. Rafiee A, Moradi MH, Farzaneh MR. Novel genetic-neuro-fuzzy filter for speckle reduction from sonography images. J Digit Imaging 2004; 17(4):292-300.
7. Kalmanson D, Stegall HF. Cardiovasculer investigations and fuzzy set theory. American Journal of Cardiology 1975; 35:80¬84.
8. Guo Z, Durand LG, Allard L, Cloutier G, Lee HC, Langlois
YE. Cardiac Doppler blood flow signal analysis. Part II:The timefrequency distribution by using autoregressive modeling. Med Biol Eng Comput 1993; 31:242-248.
9. Degani R. Bortolan G. Fuzzy decision-makingin electrocar-diography. Artificial Intelligence in Medicine 1898; 87-91.
10. Kere EE. Outline of an expert system for ECG diagnosis using fuzzy sets. Artificial Intelligence in Medicine 1989; 3:139-144.
11. Hudson DL, Cohen, ME, Deedwania PC. A hybrid system for diagnosis and treatment of heart disease. Medicine Biology Society 1994; 1368-1369.
12. Baxt WG. Use of an artificial neural network for data analysis in clinical decision making: The diagnosis of acute coronary occlusion. Neural Computation 1990; 2,480-489.
13. Akay YM, Akay M, Welkowitz W, Kostis J. Noninvasive detection of coronary artery disease. Eng in Medicine and Biology Mag 1994; 9(5):761-764.
14. Cios KJ, Goodenday LS, Shah KK, Serpen G. A novel
algorithm for classification of SPECT images of a human heart. Proc. 9th IEEE Symp. on computer-based medical systems, IEEE Comput. Soc. Press, Los Alamitos 1996; 1-5.
15. Jain R, Mazumdar J, Moran W. Application of fuzzy classifier system to coronary artery disease and breast cancer. Australasian Physical Engineering Sciences in Medicine 1998; 21(3):141-147.
16. Nauck D, Kruse R. Obtaining interpretable fuzzy classification rules from medical data. Artificial Intelligence in Medicine 1999;16:149-169.
17. Güler İ, Hardalaç F, Barışçı, N. Application of FFT analyzed Cardiac Doppler Signals To Fuzzy Algorithm. Computers in Biology and Medicine 2002; 32:435-444.
18. Güler İ, Hardalaç F, Ergu, U, Barışçı N. Classification of Aorta Doppler signals using variable coded-hierarchical genetic fuzzy system. Expert Systems with Applications 2004; 26:321-333.
19.
Uçma
n E. Transcranial Doppler İşaretlerinin Yapay Zeka Ortamında Sınıflandırılması. Gazi Üniversitesi Fen Bilimleri
Enstitüsü Doktora Tezi 2005; 69.
20. Leung SC, Fulcher J. Classification of user expertise level by neural networks. Int J Neural Syst 1997; 8(2):155-71.
21. Heiss JE, Held CM, Estevez PA, Perez CA, Holzmann CA,
Perez JP.Classification of sleep stages in infants: a neuro fuzzy approach. Eng Med Biol Mag 2002; 21(5):147-51.
22. Atacak İ. Genel Amaçlı Bir Bulanık Mantık Denetleyicinin Tasarımı. Gazi Üniversitesi Fen Bilimleri Enstitüsü Yüksek Lisans Tezi 1998; 71.
Serhatlıoğlu ve Hardalaç
23. Nauck D, Klawonn F, Kruse R. Foundations of neuro-fuzzy systems. Wiley Chichester 1997; 187-221.
24. Williams R, Neural Network Learning and Application.
Addison-Wesley 1989; 1-212.
25.
Ergü
n U, Hardalaç F, Güler İ. Geri yayılım sinir ağlarını kullanarak transcranial Doppler işaretlerinin sınıflandırılması. Biyomedikal Mühendisliği Ulusal Toplantısı Biyomut 2002;
111-114.
26. Basheer IA, Hajmeer M. Artificial neural networks: Fundamentals, computing, design, and application. Journal of
Microbiological Methods 2000; 43:3-31.
27. Baxt WG. Use of an artificial neural network for data analysis in clinical decision making:The diagnosis of acute coronary occlusion. Neural Computation 1990; 2;480-489.
28. Baxt WG. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Intern Med 1991; 1; 115(11):
843-8.
29. Baxt WG. A neural network trained to identify the presence of myocardial infarction bases some decisions on clinical associations that differ from accepted clinical teaching. Med
Decis Making 1994; 14(3):217-22.
30. Baxt WG, Shofer FS, Sites FD, Hollander JE. A neural
computational aid to the diagnosis of acute myocardial
infarction. Ann Emerg Med 2002; 39(4):366-73.
31. Hollander JE, Sease KL, Sparano DM, Sites FD, Shofer FS, Baxt WG. Effects of neural network feedback to physicians on admit/discharge decision for emergency department patients with chest pain. Ann Emerg Med 2004; 44(3):199-205.
32. Nauck D, Kruse R. NEFCLASS-X: A soft computing tool to build readable fuzzy classifiers. BT Technology Journal 1998; 6(3):180-190.
33. Kaps M, Damian MS, Teschendorf U, Dorndorf W.
Transcranial Doppler ultrasound findings in middle cerebral
artery occlusion. Stroke1990; 21:532-537.
34. Demchuk AM, Christo I, Wein T, Felberg RA, Malkoff M,
Grotta JC, Alexandrov AV. Specific transcranial Doppler flow findings related to the presence and site of arterial occlusion. Stroke 2000; 31:140-146.
35. Lupetin AR, Davis DA, Beckman I, Dash N. Transcranial Doppler sonography part 1. principles technique and normal appearances. Radiographics 1995; 15:179-191.
36. Bishop CCR, Powell S, Rutt D, Browse NL. Transcranial
Doppler measurement of middle cerebral artery blood flow
velocity: a validation study. Stroke 1986; 17:913-915.
37. Haykin S. Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company 1994;1-60.
38. Basheer IA, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. Journal of
Microbiological Methods 2000; 43:3-31.
39. Tafeit E, Reibnegger G. Artificial neural networks in laboratory medicine and medical outcome prediction. Clinical
Chemistry and Laboratory Medicine 1999; 37(9):845-853.
40. Lim CP, Harrison RF, Kennedy RL. Application of autonomous neural network systems to medical pattern classification tasks. Artificial Intelligence in Medicine 1997;
11:215-239.
41. Baxt WG. Use of an artificial neural network for data analysis in clinical decision making:the diagnosis of acute coronary occlusion. Neural Computation 1990; 2:480-489.
5
Fırat Tıp Dergisi 2009;14(1): 01-06
42. Allen J, Murray A. Development of a neural network screening aid fordiagnosing tower limb peripheral vascular disease from photoelectric plethysmography pulse waveforms. Physiological Measurement 1993; 14:13-22.
43. Alien J, Murray A. Prospective assessment of an artificial neural network for the detection of peripheral vascular disease from lower limb pulse waveforms. Physiological Measurement 1995; 16:39-42.
44.
Ergü
n U, Hardalaç F, Güler L Geri yayılım sinir ağlarını kullanarak transcranial Doppler işaretlerinin sınıflandırılması. Biyomedikal Mühendisliği Ulusal Toplantısı Biyomut 2002;111-114.
45. Barışcı N, Ergun U, Ilkay E, Serhatlyoglu S, Hardalac F, Guler I. Classification of mitral insufficiency and stenosis using MLP neural network and neuro-fuzzy system. J Med Syst. 2004; 28(5):423-36.
46. Akay M. Non-invasive diagnosis of coronary artery disease using a neural network algorithm. Biological Cybernetics 1992; 67:361-367.
47. Mobley BA, Schechter E, Moore WE, McKee PA, Eichner JE. Predictions of coronary artery stenosis by artificial neural net¬work. Artificial Intelligence in Medicine 2000; 18: 187-203.
48. Ergün U, Serhatlioglu S, Hardalaç F, Güler I. Classification of carotid artery stenosis of the patients with diabetes by neural network and logistic regression. Computers in Biology and
Medicine 2004; 34:389-405.
49. Wright IA, Gough NAJ. Artificial neural network analysis of common femoral artery Doppler shift signals:Classification of proximal disease. Ultrasound in Medical Biology 1999;
24(5):735-743.
50. Baxt WG. Application of artificial neural networks to clinical medicine. Lancet 1995; 346:1135-1138.
51. Güler I, Hardalaç F, Ergun U, Barışçı N. Classification of aorta Doppler signals using variable coded-hierarchical genetic fuzzy system. Expert Systems with Applications 2004;
26:321-333.
Serhatlıoğlu ve Hardalaç
52. Heckerling PS, Gerber BS, Tape TG, Wigton RS. Selection of predictor variables for pneumonia using neural networks and genetic algorithms. Methods Inf Med 2005; 44(1):89-97.
53. Gosling RG, King DH. Arterial assessment by Doppler shift ultrasound. Proceeding of the Royal Society of Medicine
1974; 67:447-449.
54. Goldberg DE, Samanti MP. Engineering optimization via genetic algorithm. Proceedings of the Ninth Conference on Electronic Computation 1986; 471-482.
55. Goldberg DE. Genetic Algorithms in Search, Optimization Machine Learning. Addison-Wesley 1989; 1-411.
56. Booker LB, Goldberg DE, Holland JH. Classifier systems and genetic algorithms. Artificial Intelligence 1989;40:235-282.
57. Rafiee A, Moradi MH, Farzaneh MR. Novel genetic-neuro-fuzzy filter for speckle reduction from sonography images. J
Digit Imaging 2004; 17(4):292-300.
58. Serhatlioglu S, Bozgeyik Z, Ozkan Y, Hardalac F, Guler I. Neurofuzzy classification of the effect of diabetes mellitus on
carotid artery. J Med Syst. 2003; 27(5):457-64.
59. Serhatlioglu S, Hardalac F, Kiris A, Ozdemir H, Yilmaz T, Guler I. A neurofuzzy classification system for the effects of diabetes mellitus on ophtalmic artery. J Med Syst. 2004; 28(2):167-76.
60. Hardalac F, Ozan AT, Barisci N, Ergun U, Serhatlioglu S, Guler I. The examination of the effects of obesity on a number of arteries and body mass index by using expert systems. J Med Syst. 2004; 28(2):129-42.
61. Serhatlioglu S, Hardalac F, Guler I. Classification of transcranial Doppler signals using artificial neural network. J Med Syst.
2003; 27(2):205-14.
62. Serhatlioglu S, Burma O, Hardalac F, Guler I. Determination of coronary failure with the application of FFT and AR methods. J Med Syst. 200; 27(2):121-31.