BibTex RIS Kaynak Göster

Techniques of Artificial Intelligence Network (Ann) and Applıed to Radiology

Yıl 2009, Cilt: 14 Sayı: 1, 1 - 6, 01.02.2009

Öz

Artifical Neural Network (ANN) have been begin by using in various field and worked to build by developing of computer process to smilar thing of human. In this cases, Introducing of Neural network techniques were aimed to composed of estimation on relationship ideas by using radiology.

Kaynakça

  • Moe MC, Westerlund 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, Dikmen M, Özdemir E. Yapay sinir ağları ve
  • 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.
  • 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.
  • 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.
  • 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.
  • Nauck D, Kruse R. Obtaining interpretable fuzzy classification rules from medical data. Artificial Intelligence in Medicine 1999;16:149-169.
  • 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.
  • 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.
  • Uçman E. Transcranial Doppler İşaretlerinin Yapay Zeka Ortamında Sınıflandırılması. Gazi Üniversitesi Fen Bilimleri Enstitüsü Doktora Tezi 2005; 69.
  • Leung SC, Fulcher J. Classification of user expertise level by neural networks. Int J Neural Syst 1997; 8(2):155-71.
  • 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.
  • Atacak İ. Genel Amaçlı Bir Bulanık Mantık Denetleyicinin Tasarımı. Gazi Üniversitesi Fen Bilimleri Enstitüsü Yüksek Lisans Tezi 1998; 71. Nauck D, Klawonn F, Kruse R. Foundations of neuro-fuzzy systems. Wiley Chichester 1997; 187-221.
  • Williams R, Neural Network Learning and Application. Addison-Wesley 1989; 1-212.
  • Ergün U, Hardalaç F, Güler İ. Geri yayılım sinir ağlarını kullanarak transcranial Doppler işaretlerinin sınıflandırılması. Biyomedikal Mühendisliği Ulusal Toplantısı Biyomut 2002; 111-114.
  • Basheer IA, Hajmeer M. Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods 2000; 43:3-31. 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.
  • Baxt WG. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Intern Med 1991; 1; 115(11): 843-8.
  • 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.
  • 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.
  • 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.
  • Nauck D, Kruse R. NEFCLASS-X: A soft computing tool to build readable fuzzy classifiers. BT Technology Journal 1998; 6(3):180-190.
  • Kaps M, Damian MS, Teschendorf U, Dorndorf W. Transcranial Doppler ultrasound findings in middle cerebral artery occlusion. Stroke1990; 21:532-537.
  • 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.
  • Lupetin AR, Davis DA, Beckman I, Dash N. Transcranial Doppler sonography part 1. principles technique and normal appearances. Radiographics 1995; 15:179-191.
  • 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.
  • Haykin S. Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company 1994;1-60.
  • Basheer IA, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods 2000; 43:3-31.
  • Tafeit E, Reibnegger G. Artificial neural networks in laboratory medicine and medical outcome prediction. Clinical Chemistry and Laboratory Medicine 1999; 37(9):845-853.
  • 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.
  • 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. Allen J, Murray A. Development of a neural network screening aid fordiagnosing tower limb peripheral vascular disease from photoelectric plethysrnography pulse waveforms. Physiological Measurement 1993; 14:13-22.
  • 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.
  • Ergün U, Hardalaç F, Güler İ. Geri yayılım sinir ağlarını kullanarak transcranial Doppler işaretlerinin sınıflandırılması. Biyomedikal Mühendisliği Ulusal Toplantısı Biyomut 2002;111-114.
  • 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.
  • Akay M. Non-invasive diagnosis of coronary artery disease using a neural network algorithm. Biological Cybernetics 1992; 67:361-367. 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. 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.
  • 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.
  • Baxt WG. Application of artificial neural networks to clinical medicine. Lancet 1995; 346:1135-1138.
  • 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. 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.
  • Gosling RG, King DH. Arterial assessment by Doppler shift ultrasound. Proceeding of the Royal Society of Medicine 1974; 67:447-449.
  • Goldberg DE, Samanti MP. Engineering optimization via genetic algorithm. Proceedings of the Ninth Conference on Electronic Computation 1986; 471-482.
  • Goldberg DE. Genetic Algorithms in Search, Optimization Machine Learning. Addison-Wesley 1989; 1-411. Booker LB, Goldberg DE, Holland JH. Classifier systems and genetic algorithms. Artificial Intelligence 1989;40:235-282.
  • 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.
  • 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.
  • 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.
  • 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.
  • Serhatlioglu S, Hardalac F, Guler I. Classification oftranscranial Doppler signals using artificial neural network. J Med Syst. 2003; 27(2):205-14.
  • 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.
  • Kabul Tarihi: 29.05.2008

Yapay Zeka Teknikleri ve Radyolojiye Uygulanması

Yıl 2009, Cilt: 14 Sayı: 1, 1 - 6, 01.02.2009

Öz

İnsanın düşünme yapısının benzerini bilgisayar işlemlerini geliştirerek yapmaya çalışmak olarak tanımlanmakta olan yapay zeka günümüzde birçok alanda yaygın bir şekilde kullanılmaya başlanmıştır. Bu makalede yapay zeka teknikleri tanıtılarak bu tekniklerin radyolojide kullanımlarına ilişkin görüşler ortaya konulması amaçlanmıştır.

Kaynakça

  • Moe MC, Westerlund 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, Dikmen M, Özdemir E. Yapay sinir ağları ve
  • 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.
  • 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.
  • 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.
  • 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.
  • Nauck D, Kruse R. Obtaining interpretable fuzzy classification rules from medical data. Artificial Intelligence in Medicine 1999;16:149-169.
  • 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.
  • 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.
  • Uçman E. Transcranial Doppler İşaretlerinin Yapay Zeka Ortamında Sınıflandırılması. Gazi Üniversitesi Fen Bilimleri Enstitüsü Doktora Tezi 2005; 69.
  • Leung SC, Fulcher J. Classification of user expertise level by neural networks. Int J Neural Syst 1997; 8(2):155-71.
  • 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.
  • Atacak İ. Genel Amaçlı Bir Bulanık Mantık Denetleyicinin Tasarımı. Gazi Üniversitesi Fen Bilimleri Enstitüsü Yüksek Lisans Tezi 1998; 71. Nauck D, Klawonn F, Kruse R. Foundations of neuro-fuzzy systems. Wiley Chichester 1997; 187-221.
  • Williams R, Neural Network Learning and Application. Addison-Wesley 1989; 1-212.
  • Ergün U, Hardalaç F, Güler İ. Geri yayılım sinir ağlarını kullanarak transcranial Doppler işaretlerinin sınıflandırılması. Biyomedikal Mühendisliği Ulusal Toplantısı Biyomut 2002; 111-114.
  • Basheer IA, Hajmeer M. Artificial neural networks: Fundamentals, computing, design, and application. Journal of Microbiological Methods 2000; 43:3-31. 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.
  • Baxt WG. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Intern Med 1991; 1; 115(11): 843-8.
  • 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.
  • 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.
  • 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.
  • Nauck D, Kruse R. NEFCLASS-X: A soft computing tool to build readable fuzzy classifiers. BT Technology Journal 1998; 6(3):180-190.
  • Kaps M, Damian MS, Teschendorf U, Dorndorf W. Transcranial Doppler ultrasound findings in middle cerebral artery occlusion. Stroke1990; 21:532-537.
  • 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.
  • Lupetin AR, Davis DA, Beckman I, Dash N. Transcranial Doppler sonography part 1. principles technique and normal appearances. Radiographics 1995; 15:179-191.
  • 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.
  • Haykin S. Neural Networks: A Comprehensive Foundation. Macmillan College Publishing Company 1994;1-60.
  • Basheer IA, Hajmeer M. Artificial neural networks: fundamentals, computing, design, and application. Journal of Microbiological Methods 2000; 43:3-31.
  • Tafeit E, Reibnegger G. Artificial neural networks in laboratory medicine and medical outcome prediction. Clinical Chemistry and Laboratory Medicine 1999; 37(9):845-853.
  • 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.
  • 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. Allen J, Murray A. Development of a neural network screening aid fordiagnosing tower limb peripheral vascular disease from photoelectric plethysrnography pulse waveforms. Physiological Measurement 1993; 14:13-22.
  • 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.
  • Ergün U, Hardalaç F, Güler İ. Geri yayılım sinir ağlarını kullanarak transcranial Doppler işaretlerinin sınıflandırılması. Biyomedikal Mühendisliği Ulusal Toplantısı Biyomut 2002;111-114.
  • 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.
  • Akay M. Non-invasive diagnosis of coronary artery disease using a neural network algorithm. Biological Cybernetics 1992; 67:361-367. 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. 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.
  • 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.
  • Baxt WG. Application of artificial neural networks to clinical medicine. Lancet 1995; 346:1135-1138.
  • 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. 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.
  • Gosling RG, King DH. Arterial assessment by Doppler shift ultrasound. Proceeding of the Royal Society of Medicine 1974; 67:447-449.
  • Goldberg DE, Samanti MP. Engineering optimization via genetic algorithm. Proceedings of the Ninth Conference on Electronic Computation 1986; 471-482.
  • Goldberg DE. Genetic Algorithms in Search, Optimization Machine Learning. Addison-Wesley 1989; 1-411. Booker LB, Goldberg DE, Holland JH. Classifier systems and genetic algorithms. Artificial Intelligence 1989;40:235-282.
  • 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.
  • 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.
  • 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.
  • 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.
  • Serhatlioglu S, Hardalac F, Guler I. Classification oftranscranial Doppler signals using artificial neural network. J Med Syst. 2003; 27(2):205-14.
  • 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.
  • Kabul Tarihi: 29.05.2008
Toplam 49 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Selami Serhatlıoğlu Bu kişi benim

Fırat Hardalaç Bu kişi benim

Yayımlanma Tarihi 1 Şubat 2009
Yayımlandığı Sayı Yıl 2009 Cilt: 14 Sayı: 1

Kaynak Göster

APA Serhatlıoğlu, S., & Hardalaç, F. (2009). Yapay Zeka Teknikleri ve Radyolojiye Uygulanması. Fırat Tıp Dergisi, 14(1), 1-6.
AMA Serhatlıoğlu S, Hardalaç F. Yapay Zeka Teknikleri ve Radyolojiye Uygulanması. Fırat Tıp Dergisi. Şubat 2009;14(1):1-6.
Chicago Serhatlıoğlu, Selami, ve Fırat Hardalaç. “Yapay Zeka Teknikleri Ve Radyolojiye Uygulanması”. Fırat Tıp Dergisi 14, sy. 1 (Şubat 2009): 1-6.
EndNote Serhatlıoğlu S, Hardalaç F (01 Şubat 2009) Yapay Zeka Teknikleri ve Radyolojiye Uygulanması. Fırat Tıp Dergisi 14 1 1–6.
IEEE S. Serhatlıoğlu ve F. Hardalaç, “Yapay Zeka Teknikleri ve Radyolojiye Uygulanması”, Fırat Tıp Dergisi, c. 14, sy. 1, ss. 1–6, 2009.
ISNAD Serhatlıoğlu, Selami - Hardalaç, Fırat. “Yapay Zeka Teknikleri Ve Radyolojiye Uygulanması”. Fırat Tıp Dergisi 14/1 (Şubat 2009), 1-6.
JAMA Serhatlıoğlu S, Hardalaç F. Yapay Zeka Teknikleri ve Radyolojiye Uygulanması. Fırat Tıp Dergisi. 2009;14:1–6.
MLA Serhatlıoğlu, Selami ve Fırat Hardalaç. “Yapay Zeka Teknikleri Ve Radyolojiye Uygulanması”. Fırat Tıp Dergisi, c. 14, sy. 1, 2009, ss. 1-6.
Vancouver Serhatlıoğlu S, Hardalaç F. Yapay Zeka Teknikleri ve Radyolojiye Uygulanması. Fırat Tıp Dergisi. 2009;14(1):1-6.