在目前的肝臟的病變上例如肝癌等等所做的臨床檢測技術，有一些是依賴肝臟的切片取樣方式來做檢驗。隨著醫療技術的進步，近幾年來常利用非侵入式的檢驗技術方式來檢查(如超音波影像顯示的方法)，使得肝臟病變檢查變的比以往更加便利。然而對於經驗較少的醫師，於超音波影像中較不易以肉眼清楚辨識出肝纖維化、肝細胞癌及肝膿瘍，造成不易區分或診斷的情形。
　　為了解決這個問題，我們擬採用影像處理的技術，利用影像處理演算法以及圖形辨識之電腦輔助來判斷肝臟疾病。除此之外，也希望藉由找出影像處理的分類參數，提高對超音波影像的判別，使得在診斷肝臟疾病的準確率提高，以此研究結果輔助肝臟超音波的臨床診斷。於本研究中，引用了以下方法進行研究與分析，包括了Cell-based 特徵、D2-variance、有效小波係數三角化、主成份分析、灰階共生矩陣、支持向量機、模糊支持向量機及模糊類神經網路。透過適當的特徵擷取與分類器的應用，能夠有效改善判斷肝臟疾病之辨識率，並應用於電腦輔助分類設計。

Some of the clinical technologies of detecting liver disease, such as liver cancer, are dependent on liver biopsy sampling. With medical technology advances in recent years, people have used non-invasive detecting methods to diagnose the disease, such as ultrasound liver image, so that the liver disease detection becomes more convenient than before. However, for the less experience clincians, it perhaps will not easy to clearly identify the kind of disease from liver fibrosis、hepatocellular carcinoma and liver abscess just by eyes. Therefore we may encounter some difficulties while distinguishing or diagnosing the ultrasound image.
To solve this problem, we intend to adopt the technologies of image processing, algorithms, and pattern recognition to achieve the computer-assisted classification of the liver diseases judgement. In this study, the following methods are used for research and analysis; including Cell-based features, Decomposition-2 variance (D2-variance), Effective Wavelet Coefficients Triangulation (EWCT), Principal Component Analysis (PCA), Gray Level Co-occurrence Matrix (GLCM), Support Vector Machine (SVM), Fuzzy Support Vector Machine (FSVM), and Fuzzy Neural Network (FNN). By suitable selecting the features and using the classifiers, we can improve the accuracy of the judgement for liver diseases. The proposed methods can be used for the design of Computer-Aided classification.

P.-M. Yang, G.-T. Huang, and J.-T. Lin, “Ultrasonography in the diagnosis of benigh diffuse parenchymal liver diseases: A prospective study,” Journal of the Formosan Medical Association, vol. 87, pp. 966-977, 1988.
A.-K. Goyal, D.-S. Pokharna, and S.-K. Sharma., “Ultrasonic diagnosis of cirrhosis reference to quantitative of hepatic dimensions,”Gastrointest Radiol, vol. 15, pp. 32-34, 1990.
V. Vilgran, D. Lebree, Y. Menu, A. Scherrer, and H. Nahum,“Comparison between ultrasonographic signs and the degree of portal hypertension in patients with cirrhosis,”Gastrointest Radiol, vol. 15, pp. 218-222, 1990.
A.-E.-A. Joseph, S.-H. Saverymuttu, S. Al-Sam, M.-G. Cook, and J.-D. Maxwell, “Comparison of liver histilogy with ultrasonography in assessing diffuse parenchymal liver disease,” Clinical Radiology, vol. 43, pp. 26-31, 1991.
Ishak, A. Baptista, and L. Bianchi, “Histological grading and staging of chronic hepatitis,”Journal of Hepatology, vol. 22, pp. 696-699, 1995.
Y.-M. Kadah and A.-A. Farag, “Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images,” IEEE Transactions on Medical Imaging, vol. 15, pp. 466-478, 1996.
L. Sandrin, B. Fourquet, and M. Hasquenoph, “Transient elastography: a new noninvasive method for assessment of hepatic fibrosis,” Ultrasound Medicine Biology, vo1. 29, pp. 1705-1713, 2003.
P.-E.-J. Chang, H.-F. Lui, and Y.-P. Chau, “A prospective study of the diagnostic accuracy of transient elastography in the evaluation of hepatic fibrosis in asians compared with liver biopsy and APRI,” Journal of Hepatology, vol. 48, pp. 239-240, 2008.
M. Ziol, A. Handra-Luca, and A. Kettaneh, “Noninvasive assessment of liver fibrosis by measurement of stiffness in patients with chronic hepatitis C,” Hepatology, vol. 41, pp. 48-54, 2005.
M. Sanada, M. Ebara, and H. Fukuda, “Clinical evaluation of sonoelasticity measurement in liver using ultrasonic imaging of internal forced low-frequency vibration,” Ultrasound Medicine Biology, vol. 26, pp. 1455-1460, 2000.
W.-C. Yeh, S.-W. Huang, and P.-C. Li, “Liver fibrosis grade classification with B-mode ultrasound,” Ultrasound Medicine Biology, vol. 29, pp. 1229-1235, 2003.
W.-C. Yeh, “Ultrasonic Tissue Characterization of Liver,” National Taiwan University , 2005.
M. Meziri, W.-C.-A. Pereira, A. Abdelwahab, C. Degott, and P. Laugier, “In vitro chronic hepatic disease characterization with a multiparametric ultrasonic approach”, Ultrasonics, vol. 43, pp. 305-313, 2005.
M. Meziri, R. Bouzitoune, C.-B.Machado, W. Coelho de, A. Pereira, F. Padilla, P. Laugier, and N. Tiah, “Multiparametric study to identify the hepatic fibrosis stages,” IEEE International Ultrasonics, pp. 2236-2239, 2009.
M. Meziri, R. Bouzitoune, C.-B.Machado, W. Coelho de, A. Pereira, F. Padilla, P. Laugier, and N. Tiah, “Multiparametric study to identify the hepatic fibrosis stages,” IEEE International Ultrasonics, pp. 2236-2239, 2009.
M. Tanter, M. Pernot, G. Montaldo, J.-L. Gennisson, E. Bavu, E. Mace, T.-M. Nguyen, M. Couade, and M. Fink, “Real time quantitative elastography using supersonic shear wave Imaging,” 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 276-279, 2010.
S. Audiere, Y. Mofid, M. Charbit, E. Angelini, V. Miette, and L. Sandrin, “Fibroscan practice improvement with a real-time assistance ultrasound tool: a premiminary study,” 2009 IEEE International Ultrasonics Symposium (IUS), pp. 1455 -1458, 2009.
S. Gaiani, L. Gramantieri, and N. Venturili, “What is the criterion for differentiating chronic hepatits from compensated cirrhosis? A prospective study comparing ultrasonography and percutaneous liver biopsy,”Journal of Hepatology, vol. 27, pp. 979-985, 1997.
G. Cao, P. Shi, “Liver Fibrosis Identification Based on Ultrasound Images,” Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September, 2005, pp. 1-4.
T. Nishiura, H. Watanabe, M. Ito, Y. Matsuoka, K. Yano, M. Daikoku, H. Yatsuhashi, K. Dohmen, and H. Ishibashi, “Ultrasound evaluation of the fibrosis stage in chronic liver disease by the simultaneous use of low and high frequency probes,” The British Journal of Radiology, vol.78, pp. 189-197, 2005.
C. Aube, F. Oberti, and N. Korali, “Ultrasonographic of hepatic fibrosis or cirrhosis,”Journal of Hepatology, vol. 30, pp. 472-478, 1999.
G.-M. Dahab, M.-M. Kheriza, H.-M. EL-Beltagl, A.-M.-M. Fouda, and O.-A. Sharafel-Din, “Digital quantification of fibrosis in liver biopsy sections: description of a new method by photoshop software,” Journal of Gastroenterology and Hepatology, pp. 78-85, 2004.
P.-E.-J. Chang, H.-F. Lui, and Y.-P. Chau, “A prospective study of the diagnostic accuracy of transient elastography in the evaluation of hepatic fibrosis in asians compared with liver biopsy and APRI,” Journal of Hepatology, vol. 48, pp. 239-240, 2008.
J. Shao, J. Wang, K. Liu, J. Bai, X. Hu, and L. Qian, “Maximal accumulative respiration strain for the assessment of hepatic fibrosis,” IEEE International Ultrasonics, pp. 2029 -2032, 2008.
S. S.-D. Xu*, C.-C. Chang, C.-T. Su, C.-C. Ye, M.-H. Yang, Y.-P. Wu, Y.-K. Liu, and H.-Y. Lou, “Computer-aided classification of liver fibrosis based on ultrasoundimage processing,” Journal of Biobased Materials and Bioenergy, accepted 2013. (SCI)
W.-B. Wooten, B. Green, and H.-M. Goldstein, “Ultrasonography of necrotic hepatic metastasis,” Radiology, vol. 128, pp. 447-450, 1978.
N. Newlin and T.-M. Silver, “Ultrasonic features of pyogenic liver abscesses,” Radiology, vol. 139, pp. 155-159, 1981.
S.-Y. Chou, K.-C. Yang, and C.-R. Kao, “Ultrasonographic features of liver abscess,” Journal of Ultrasound in Medicine, vol. 2, pp. 117-122, 1985.
S.-Y. Chou, “Ultrasonographic features of liver abscess,” Journal of Ultrasound in Medicine, vol. 2, pp. 117-122, 1985.
C.-T. Chiu, “A clinical study on pyogenic liver abscess,” Journal of the Formosan Medical Association, vol. 86, pp. 405-412, 1987.
M. Atri, J. Stemple, and P.-M. Bret, “Incidence of portal vein thrombosis complicating liver metastasis as detected by duplex ultrasound,” Journal of Ultrasound in Medicine, vol. 9, pp. 285-289, 1990.
X.-Y. Zhang, X.-F. Diao, T.-F. Wang, and S.-P. Chen, “Study on feature extraction for ultrasonic differentiation of liver space-occupying lesions,” International Conference on Bioinformatics and Biomedical Engineering (ICBBE), pp. 1-4, 2010.
D.-Q. MA, “Imaging Diagnosis,” Beijing medical university press, Beijing, 2009.
R. Lencioni, D. Cioni, and C. Della Pina, “Imaging diagnosis,” Seminars in Liver Disease, vol. 25, pp. 162-170, 2005.
趙世晃，腹部超音波速成，合記圖書出版社，1991。
J.-L. Sung, “Clinical study on primary carcinoma of the liver in Taiwan,” The American Journal of Digestive Diseases, vol. 12, p.p. 1036-1049, 1967.
M.-C. Kew, H.-A. Dos Santos, and S. Sherlock, “Diagnosis of primary cancer of the liver,” British Medical Journal, vol. 4, no. 784, pp. 408-411, 1971.
D.-S. Chen, J.-L. Sung, and J.-C. Sheu, “Small hepatocellular carcinoma-a clinicopathological study in thirteen patients,” Gastroenterology, vol. 83, no. 5, pp. 1109-1119, 1982.
D.-S. Chen, J.-L. Sung, and J.-C. Sheu, “Ultrasonography of small hepatic tumors using high-resolution linear-array real-time instruments,” Radiology, vol. 150, no. 3, pp. 797-802, 1984.
D.-S. Chen, “Serum α-fetoprotein in the early stage of human hepatocellular carcinoma,” Gastroenterology, vol. 86, pp. 1404-1409, 1984.
D.-S. Chen, J.-L. Sung, and J.-C. Sheu, “Hepatocellular carcinoma : US evolution in the early stage,” Radiology, vol. 155, no. 2, vol. 463-467, 1985.
J.-C. Shen, “Growth rate of asymptomatic hepatocellular carcinoma and its implications,” Gastroenterology, vol. 89, pp. 259-266, 1985.
C.-S. Lee, “Surgical treatment of 109 patients with symptomatic and asymptomatic hepatocellular carcinoma,” Surgery, vol. 99, pp. 481-490, 1986.
J.-C. Sheu, “Small hepatocellular carcinoma: intratumor ethanol treatment using new needle and guidance system,” Radiology, vol. 163, pp. 43-48, 1987.
Y.-M. Tsang, “Transcatheter arterial embolization for the treatment of hepatocellular carcinoma,” Journal of the Formosan Medical Association, vol. 86, pp. 606-614, 1987.
S. Tanaka, T. Kitamura, and M. Fujita, “Color Doppler flow imaging of liver tumors,” American Journalism Review, vol. 154, no. 3, pp. 509-514, 1990.
K. Wernecke, L. Henke, and P. Vassallo, “Pathologic explanation for hypoechoic halo seen on sonograms of malignant liver tumors: an in vitro correlative study,” American Journalism Review, vol.159, pp. 1011-1016, 1992.
M. Kadoya, O. Matsui, T. Takashima, and A. Nonomura, “Hepatocellular carcinoma : correlation of MR imaging and histopathologic findings,” Radiology, vol. 183, no. 3, pp. 819-825, 1992.
D. Pateron, N. Ganne, and J.-C. Trinchet, “Prospective study of screening for hepatocellular carcinoma in caicasoan patients with cirrhosis,” Journal of Hepatology, vol. 20, no. 1, pp. 65-71, 1994.
G.-T. Huang, J.-C. Sheu, and P.-M. Yang, “Ultrasound-guided cutting biopsy for the diagnosis of hepatocellular carcinoma- a study based on 420 patients,” Journal of Hepatology, vol. 25, no. 3, pp. 334-338, 1996.
J.-H. Oliver 3rd, R.-L. Baron, M.-P. Federle, and H.-E. Rockette Jr, “Detecting hepatocellular carcinoma : value of unenhanced or arterial phase CT imaging or both used in conjunction with conventional portal venous phase contrast enhanced CT imaging,” American Journal of Roentgenology, vol. 167, no. 1, pp. 71-77, 1996.
M. Kudo, “Imaging diagnosis of hepatocellular carcinoma and premalignant/borderline lesions,” Seminars in Liver Disease, vol. 19, no. 3, pp. 297-309, 1999.
H. Bismuth, “Liver transplantation for hepatocellular carcinoma,” Seminars in Liver Disease, vol. 19, pp. 311, 1999.
S. Okuda, “Local ablation of hepatocellular carcinoma,” Seminars in Liver Disease, vol. 19, pp. 323, 1999.
M.-J. Tong, L.-M. Blatt, and V.-W. Kao, “Surveillance for hepatocellular carcinoma in patients with chronic viral states of America,” Journal of Gastroenterology and Hepatology, vol. 16, no. 5, pp. 553-559, 2001.
D. Pauleit, J. Textor, and R. Bachmann, “Hepatocellular carcinoma : detection with gadolinium- and ferumoxides-enhanced MR imaging of the liver,” Radiology, vol. 222, no. 1, pp. 73-80, 2002.
V. Donckier, “New consideration for an overall approach to treat hepatocellular carcinoma in cirrhotic patients,” Journal of Surgical Oncology, vol. 84, no. 1, pp. 36, 2003.
A. Laghi, R. Iannaccone, and P. Rossi, “Hepatocellular carcinoma : detection with triple-phase multi-detector row helical CT in patients with chronic hepatitis,” Radiology, vol. 226, no. 2, pp. 543-549, 2003.
B.-H. Lang, R.-T. Poon, S.-T. Fan, and J. Wong, “Outcomes of patients with hepatocellular carcinoma presenting with variceal bleeding,” The American Journal of Gastroenterology, vol. 99, no. 11, pp. 2158-2165, 2004.
B.-I. Choi, “The current status of imaging diagnosis of hepatocellular carcinoma,” Liver Transplantation, vol. 10, pp. S20-S25, 2004.
J.-A. Marrero and A.-S. Lok, “Newer markers for hepatocellular carcinoma,” Gastroenterology, vol. 127, pp. S113-S119, 2004.
J. Bruix and M. Sherman, “Management of hepatocellular carcinoma,” Hepatology, vol. 42, pp. 1208-1236, 2005.
H. Yoshida, S. Shiina, and M. Omata, “Early liver cancer : concepts, diagnosis, and management,” International Journal of Clinical Oncology, vol. 10, pp. 384-390, 2005.
E.-C. Lai and W.-Y. Lau, “Spontaneous rupture of hepatocellular carcinoma : a systematic review,” Archives of Surgery, vol. 141, no. 2, pp. 191-198, 2006.
S.-Q. Huang, M.-Y. Ding, and S.-G. Zhang, “Feature statistic analysis of ultrasound images of liver cancer,” Proceedings of SPIE- The International Society for Optical Engineering, vol. 6789, pp. 6778-6789, 2007.
C.-F. Lin and S.-D. Wang, “ Fuzzy support vector machines”, IEEE Transactions on Neural Networks, vol. 13, no. 2, 2002.
A.-A. Miranda, Y.-A. Le Borgne, and G. Bontempi, “New routes from minimal approximation error to principal Components,” Neural Processing Letters, vol. 27, no. 3, 2008.
L.-U. Yuan, “Gait recognition based on fuzzy support vector machine,” Computer Engineering, vol. 35, no. 21, pp. 189-191, 2009.
H. Abdi and L.-J. Williams, “Principal Component Analysis Wiley Interdisciplinary Reviews: Computational Statistics,” vol. 2, pp. 433–459, 2010.
L. Lei and K.-N. Zhao, “A new intrusion detection system based on rough set theory and fuzzy support vector machine,” International Workshop on Intelligent Systems and Applications, Wuhan, May 28-29, 2011, pp. 1-5.
L. Chen, Y.-C. Yang, and M. Yao, “Reliability detection by fuzzy SVM with UBM component feature for emotional speaker recognition,” Eighth International Conference on Fuzzy Systems and Knowledge Discovery, Shanghai, July 26-28, 2011, vol. 1, pp. 458-461.
B. Lakshmanan, A. Jeril, Priscilla, S. Ponni, and V. Sankari, “Evaluation of imbalanced datasets using fuzzy support vector machine-class imbalance learning (FSVM-CIL),” IEEE-International Conference on Recent Trends in Information Technology, Chennai, Tamil Nadu, June 3-5, 2011, pp. 1131–1136.
M. Serafeim, M. Giorgos, K. Nikos, B. John, “SVM-based fuzzy decision trees for classification of high spatial resolution remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing, vol. 50, no. 1, 2012.
T. Hao, L. Yuhe, and W. Xiufeng, “A new fuzzy membership function for fuzzy support vector machine and its application in machinery fault diagnosis,” International Conference on Natural Computation, Chongqing, May 29-31, 2012, pp. 35-39.
N. Nuryani, S.-H. Ling, and H.-T. Nguyen, “Hybrid particle swarm - based fuzzy support vector machine for hypoglycemia detection,” IEEE World Congress on Computational Intelligence, Brisbane, Australia, Brisbane, QLD, June 10-15, 2012, pp. 10-15.
D.-Z. Tian, G.-B. Peng, and M.-H. Ha, “Fuzzy support vector machine based on non-equilibrium data,” International Conference on Machine Learning and Cybernetics, Xi’an, 2012, pp. 15-17.
R.-K. Sevakula and N.-K. Verma, “Fuzzy support vector machine using hausdorff distance,” IEEE International Conference on Fuzzy Systems, Hyderabad, July 7-10, 2013, pp. 1-6.
A.-D. Ashkezari, H. Ma, T.-K. Saha, and C. Ekanayake, “Application of fuzzy support vector machine for determining the health index of the insulation system of in-service power transformers,” IEEE Transactions on Dielectrics and Electrical Insulation, vol. 20, no. 3, 2013.
J.-M. Shapiro, “Embedded image coding using zerotrees of wavelet coefficients,” IEEE Transactions on Signal Processing, vol. 41, no. 12, pp. 3445-3462, 1993.
A. Munteanu, J. Cornelis, G. Van Der Auwera, and P. Cristea, “Wavelet image compression – the quadtree coding approach,” IEEE Transactions on Technology in Biomedicine, vol. 3, no. 3, pp.176-185, 1999.
W.-S. Lee, H.-J. Lee, and J.-H. Chung, “Wavelet-based FLD for face recognition,” IEEE International Midwest Symposium on Circuits and Systems, Lansing MI, pp. 734-737, 2000.
C. Liu and H. Wechsler, “Gabor feature based classification using the enhanced fisher linear discriminant model for face recognition,” IEEE Transactions on Image Processing, vol.11, pp.467-476, 2002.
D. Mitrea, S. Nedevschi, M. Lupsor, and R. Badea, “Ultrasonography contribution to hepatic steatosis quantification. possibilities of Improving this method through computerized analysis of ultrasonic image,”2006 IEEE International Conference on Automation, Quality and Testing, Robotics, Cluj-Napoca, May 25-28, 2006, vol.2, pp. 478-483.
D. Mitrea, S. Nedevschi, M. Lupsor, and R. Badea, “Exploring the textural parameters obtained from ultrasound images for modeling the liver pathological stages in the evolution towards hepatocellular carcinoma,” AQTR 2008. IEEE International Conference on Automation, Quality and Testing, Robotics, Cluj-Napoca, May 22-25, 2008, vol. 3, pp. 128-133.
D. Mitrea, S. Nedevschi, M. Lupsor, M. Socaciu, and R. Badea, “Improving the textural model of the hepatocellular carcinoma using dimensionality reduction methods,” CISP '09. 2nd International Congress on Image and Signal Processing, Tianjin, October 17-19, 2009, pp. 1-5.
D. Mitrea, S. Nedevschi, M. Lupsor, M. Socaciu, and R. Badea, “Advanced classification methods for improving the automatic diagnosis of the hepatocellular carcinoma, based on ultrasound images,” 2010 IEEE International Conference on Automation Quality and Testing Robotics (AQTR), Cluj-Napoca, Romania, May 28-30, 2010, vol. 2, pp. 1-6.
D. Mitrea, S. Nedevschi, M. Lupsor, M. Socaciu, and R. Badea, “Experimenting various classification techniques for improving the automatic diagnosis of the malignant liver tumors, based on ultrasound images,” 2010 3rd International Congress on Image and Signal Processing (CISP), Yantai, October 16-18, 2010, vol. 4, pp. 1853-1858.
D. Mitrea, S. Nedevschi, and R. Badea, “The role of the superior order GLCM in improving the automatic diagnosis of the hepatocellular carcinoma based on ultrasound images,” 2011 34th International Conference on Telecommunications and Signal Processing (TSP), Budapest, August 18-20, 2011, pp. 602-606.
D. Mitrea, S. Nedevschi, and R. Badea, “The role of the superior order GLCM and of the generalized co-occurrence matrices in the characterization and automatic diagnosis of the hepatocellular carcinoma, based on ultrasound images,” 2011 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, August 25-27, 2011, pp. 197-204.
D. Mitrea, M. Socaciu, R. Badea, and A. Golea, “Texture based characterization and automatic diagnosis of the abdominal tumors from ultrasound images using third order GLCM features,” 2011 4th International Congress on Image and Signal Processing (CISP), Shanghai, October 15-17, 2011, vol. 3, pp. 1558-1562.
D. Mitrea, S. Nedevschi, and R. Badea, “The role of the multiresolution textural features in improving the characterization and recognition of the liver tumors, based on ultrasound images,” 2012 14th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timisoara, September 26-29, 2012, pp. 192-199.
A.-A Moghe, J. Singhai, and S.-C. Shrivastava, “Elastic registration of 2D abdominal CT images using hybrid feature point selection for liver lesions,” 2010 IEEE 2nd International Advance Computing Conference (IACC), Patiala, February 19-20, 2010, pp. 337-341.
N.-M. Saad, S.-A.-R. Abu-Bakar, S. Muda, M.-M. Mokji, and L. Salahuddin, “Brain lesion segmentation of diffusion-weighted MRI using gray level co-occurrence matrix,” 2011 IEEE International Conference on Imaging Systems and Techniques (IST), Penang, May 17-18, 2011, pp. 284-289.
S.-S. Kumar, R.-S. Moni, and J.Rajeesh, “Liver tumor diagnosis by gray level and contourlet coefficients texture analysis,” 2012 International Conference on Computing, Electronics and Electrical Technologies(ICCEET), Kumaracoil, March 21-22, 2012, pp. 557-562.
A.-K. Chaudhari and J.-V. Kulkarni, “Local entropy based brain MR image segmentation,” 2013 IEEE 3rd International Advance Computing Conference (IACC), Ghaziabad, February 22-23, 2013, pp. 1229-1233.
L.-J. Huang, J. Shi, R.-L. Wang, and S.-C. Zhou, “Shearlet-based ultrasound texture features for classification of breast tumor,” 2013 Seventh International Conference on Internet Computing for Engineering and Science (ICICSE), Shanghai, September 20-22, 2013, pp. 116-121.
M. Mohamed Fathoma, D. Manimegalai and S. Thaiyalnayaki, “Automatic detection of tumor subtype in mammograms based on GLCM and DWT features using SVM,” 2013 International Conference on Information Communication and Embedded Systems(ICICES), Chennai, February 21-22, 2013, pp. 809-813.
Y.-Y. Wang, S.-C. Chang, L.-W. Wu, S.-T. Tsai, and Y.-N. Sun, “A color-based approach for automated segmentation in tumor tissue classification,” EMBS 2007. 29th Annual International Conference of the IEEE on Engineering in Medicine and Biology Society, Lyon, August 22-26, 2007, pp. 6576-6579.
Z. Luo, X. Wu, S. Guo, and B. Ye, "Diagnosis of breast cancer tumor based on PCA and fuzzy support vector machine classifier," Fourth International Conference on Natural Computation, Jinan, October 18-20, 2008, vol. 4, pp. 363-367.
W.-M. Ding, H.-L. Bu, S.-Z. Zheng, and F. Qian, “Tumor classification by using PCA with relief wrapper,” ICCSIT 2009. 2nd IEEE International Conference on Computer Science and Information Technology, Beijing, August 8-11, 2009, pp. 514-517.
S.-Y. Xie, R. Guo, N.-F. Li,G. Wang, and H.-T. Zhao, “Brain fMRI processing and classification based on combination of PCA and SVM,” IJCNN 2009. International Joint Conference on Neural Networks, Atlanta, GA, June 14-19, 2009, pp. 3384-3389.
Buciu and A. Gacsadi, “Gabor wavelet based features for medical image analysis and classification,” ISABEL 2009. 2nd International Symposium on Applied Sciences in Biomedical and Communication Technologies, Bratislava, November 24-27, 2009, pp. 1-4.
J.-L. Yang and H.-X. Li, “PCA based sequential feature space learning for gene selection,” 2010 International Conference on Machine Learning and Cybernetics (ICMLC), Qingdao, July 11-14, 2010, vol. 6, pp. 3079-3084.
H. Hasan and N.-M. Tahir, “Feature selection of breast cancer based on principal component analysis,” 2010 6th International Colloquium on Signal Processing and Its Applications (CSPA), Mallaca City, May 21-23, 2010, pp. 1-4.
C.-P. Tu, L. Gan, and Z.-P. Yu, “Based on an improved pre-PCA + LDA classifier design in tumor cells,” 2010 International Conference On Computer and Communication Technologies in Agriculture Engineering (CCTAE), Chengdu, June 12-13, 2010, vol. 2, pp. 95-98.
M.-A. Wani, “Microarray classification using sub-space grids,” 2011 10th International Conference on Machine Learning and Applications and Workshops (ICMLA), 2011, Honolulu, December 18-21, vol. 1, pp. 389-394.
Y. Su, R.-J. Wang, C.-X. Li, and P. Chen, “A dynamic subspace learning method for tumor classification using microarray gene expression data,” 2011 Seventh International Conference on Natural Computation, Shanghai, July 26-28, 2011, vol. 1, pp. 396-400.
V. Kumar, J. Sachdeva, I. Gupta, N. Khandelwal, and C.K. Ahuja, “Classification of brain tumors using PCA-ANN,” 2011 World Congress on Information and Communication Technologies (WICT), Mumbai, December 11-14, 2011, p.p. 1079-1083.
X.-N. Wang, J.-W. Zhang, Y. Xin, W.-P. Wang, and M.-C. Lian, “LCT image recognition for cervical cells based on BP neural network,” 2012 2nd International Conference on Computer Science and Network Technology (ICCSNT), Changchun, December 29-31, 2012, pp. 1479-1483.
R. Bhattacharjee and M. Chakraborty, “Brain tumor detection from MR images: image processing slicing and PCA based reconstruction,” 2012 Third International Conference on Emerging Applications of Information Technology, Kolkata, November 30- December 1, 2012, pp. 97-101.
K. Li, L. Nan, Y.-Y. Chen, and Y. Kang, “Research on automatic recognition of breast tumors based on principal component analysis,” 2012 International Conference on Information and Automation(ICIA), Shenyang, China, June 6-8, 2012, pp. 338-341.
W.-H. Ibrahim, A.-A.-A. Osman, and Y.-I. Mohamed, “MRI brain image classification using neural networks,” 2013 International Conference on Computing Engineering And Electronics Engineering(ICCEEE), Khartoum, August 26-28, 2013, pp. 253-258.
S. Goswami and L.-K.-P. Bhaiya, “Brain tumour detection using unsupervised learning based neural network,” 2013 International Conference on Communication Systems and Network Technologies (CSNT), Gwalior, April 6-8, 2013, pp. 573-577.
J.-M. Keller and Y. Hayashi, “Evidence aggregation networks for fuzzy logic inference,” IEEE Transactions on Neural Networks, vol. 3, no. 5, pp. 334-338, 1993.
C.-T. Lin and Y.-C. Lu, “A neural fuzzy system with fuzzy supervised learning,” IEEE Transactions on Systems, Man, and Cybernetics-part B: Cybernetics, vol. 26, no. 5, 1996.
J. Hua, “Application of fuzzy neural network in multi-maneuvering target tracking,” 2010 2nd International Asia Conference on Informatics in Control, Automation and Robotics, Wuhan, March 6-7, 2010, vol. 1, pp. 92-95.
R. Ballini and F. Gomide, “Recurrent fuzzy neural computation: modeling, learning and application,” IEEE International Conference on Fuzzy Systems, Barcelona, July 18-23, 2010, pp. 1-6.
Y.-Y. Fan and Y.-J. Sang, “The research of nonlinear control based on fuzzy neural network,” International Conference on Electrical and Control Engineering, Wuhan, June 25-27, 2010, pp. 2417-2420.
A.-Z. Wang and G.-F. Ren, “The design of neural network fuzzy controller in washing machine,” International Conference on Computing, Measurement, Control and Sensor Network, Taiyuan, July 7-9, 2012, pp. 136-139.
W. Jing and C.-L.-P. Chen, “Finding the near optimal learning rates of fuzzy neural networks (FNNs) via its equivalent fully connected neural networks (FFNNs),” 2012 International Conference on System Science and Engineering, Da’Lian, China, June 30-July 2, 2012, pp. 137-142.
A.-P. Lemos, W. Caminhas, and F. Gomide, “A fast learning algorithm for uniform-based fuzzy neural networks, fuzzy information processing society (NAFIPS),” 2012 Annual Meeting of the North American, pp. 1-6, 2012.
Y.-H. Huang, H. Cheng, L. Huang, and L. Sun, “Soft sensing modeling based on dynamic fuzzy neural network for penicillin fermentation,” Proceedings of the 31st Chinese Control Conference, Hefei, July 25-27, 2012, pp. 3383- 3388.
D. Xue and S. Hao, “Estimation of project costs based on fuzzy neural network,” World Congress on Information and Communication Technologies, Trivandrum, October 30-November 2, 2012, pp. 1177-1181.
Z. Wanqing, L. Kang, and G.-W. Irwin, “A new gradient descent approach for local learning of fuzzy neural models,” IEEE Transactions on Fuzzy Systems, vol. 21, no. 1, 2013.