Open Access Open Access  Restricted Access Subscription Access

A Survey on Artificial Intelligence Approaches for Medical Image Classification


Affiliations
1 Department of EEE, Anna University of Technology, Coimbatore-641047, India
 

In this paper, a survey has been made on the applications of intelligent computing techniques for diagnostic sciences in biomedical image classification. Several state-of-the-art Artificial Intelligence (AI) techniques for automation of biomedical image classification are investigated. This study gathers representative works that exhibit how AI is applied to the solution of very different problems related to different diagnostic science analysis. It also detects the methods of artificial intelligence that are used frequently together to solve the special problems of medicine. SVM neural network is used in almost all imaging modalities of medical image classification. Similarly fuzzy C means and improvements to it are important tool in segmentation of brain images. Various diagnostic studies like mammogram analysis, MRI brain analysis, bone and retinal analysis etc., using neural network approach result in use of back propagation network, probabilistic neural network, and extreme learning machine recurrently. Hybrid approach of GA and PSO are also commonly used for feature extraction and feature selection.

Keywords

Medical Imaging, Artificial Intelligence (AI), Neural Networks (NN), Fuzzy Logic (FL), Genetic Algorithms (GA), Particle Swarm Optimization (PSO)
User

  • Agus Zainal Arifin, Akira Asano, Akira Taguchi, Takashi Nakamoto, Masahiko Ohtsuka and dan Keiji Tanimoto (2005) Computer-aided system for measuring the mandibular cortical width on panoramic radiographs in osteoporosis diagnosis. Proc. SPIE Medical Imaging 2005 - Image Processing Conf. San Diego, California. pp:813-819.
  • Agus Zainal Arifin, Asano A, Taguchi A, Nakamoto T, Ohtsuka M, Tsuda M, Kudo Y and Tanimoto K (2007) Developing computer-aided osteoporosis diagnosis system using fuzzy neural network. J. Advanced Comput. Intelligence & Intelligent Informatics, 11(8), 1049-1058.
  • Aida A Ferreira, Francisco Nascimento Jr, Ing Ren Tsang, George DC Cavalcanti, Teresa B Ludermir and Ronaldo RB de Aquino (2007) Analysis of mammogram using self-organizing neural networks based on spatial isomorphism. Proc. IEEE Intl. Joint Conf. Neural Networks, Florida, USA. pp:1796-1801.
  • Andy Chiem, Adel Al-Jumaily and Rami N Khushaba (2007) A novel hybrid system for skin lesion detection. Intl. Conf. Intelligent Sensors, Sensor Networks & Information Processing. pp: 567-572.
  • AmirEhsan Lashkari (2010) A neural network based method for brain abnormality detection in MR images using gabor wavelets. Intl. J. Comput. Appl. 4(7), 9-15.
  • Anitha J, Selvathi D and Hemanth DJ (2009) Neural computing based abnormality detection in retinal optical images. IEEE Advance Comput. Conf. pp: 630-635.
  • Anitha J, Kezi Selva Vijila C and Jude Hemanth D (2009) An enhanced counter propagation neural network for abnormal retinal image classification. J. Nature & Biologically Inspired Comput. pp: 1-6.
  • Arpita Das and Mahua Bhattacharya (2008) GA based neuro fuzzy techniques for breast cancer identification. Intl. Machine Vision & Image Processing Conf. pp:136-141.
  • Ahmed Kharrat, Karim Gasmi, Mohamed Ben Messaoud, Nacéra Benamrane and Mohamed Abid (2010) A hybrid approach for automatic classification of brain MRI using genetic algorithm and support vector machine. Leonardo J. Sci. 17, 71-82.
  • Alba E, Garcia-Nieto J, Jourdan L and Talbi EG (2007) Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms. IEEE Congress on Evolutionary Comput. pp: 284-290.
  • Ahmed MN, Yamany SM, Mohamed N, Aly A Farag and T Moriarty (2002) Modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI Data. IEEE Transact. on Medical Imaging. 21(3),193-199.
  • Brijesh Verma and John Zakos (2001) Computer- Aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE Transactions on Information Technol. in Biomedicine, 5(1), 46-54.
  • Chin-Ming Hong, Chin-Teng Lin, Chao-Yen Huang and Yi-Ming Lin (2008) An intelligent fuzzy-neural diagnostic system for osteoporosis risk assessment. J. World Acad. Sci. Engg & Technol. 42, 597-602.
  • Chien-Cheng Lee, Sz-Han Chen and Yu-Chun Chiang (2007) Classification of liver disease from CT images using a support vector machine. J. Adv. Computational Intelligence & Intelligent Informatics. 11(4), 396-402.
  • Dheeba J and Tamil Selvi (2010) Bio inspired swarm algorithm for tumor detection in digital mammogram. Intl. Conf. Swarm, Evolutionary & Memetic computing. pp: 404–415.
  • Dipali M Joshi, Rana NK and Misra VM (2010) Classification of brain cancer using artificial neural network. Intl. Conf. Electronic Comput. Technol. pp: 112-116.
  • David J Krishnan, Rekha A and Sukesh Kumar (2008) Neural network based retinal image analysis. Congress on Image & Signal Processing. pp:49-53.
  • Essam Al-Daoud (2010) Cancer diagnosis using modified fuzzy network. Universal J. Comput. Sci. & Engg. Technol. 1(2), 73-78.
  • Essam A Rashed and Mohammed G Awad (2006) Neural network approach for mammography diagnosis using wavelet features. In Proc. First Can. Student Conf. Biomedical Computing. paper no.105.
  • Fatma Taher and Rachid Sammouda (2010) Artificial neural network and fuzzy clustering methods in segmenting sputum color images for lung cancer diagnosis. Intl. Conf. Signal Processing. pp: 513–520.
  • Fernandes FC, Brasil LM, JM Lamas and R Guadagnin (2010) Breast cancer image assessment using an adaptative network based fuzzy inference system. J. Pattern Recognition & Image Analysis. 20(2), 192-200.
  • Gomathi M and P Thangaraj (2010) Computer aided diagnosis system for detection of lung cancer nodules using extreme learning machine. Intl. J. Engg. Sci. & Technol. 2(10), 5770-5779.
  • Gletsos M, Mougiakakou SG, Matsopoulos GK, Nikita KS, Nikita AS and Kelekis D (2003) A computer-aided diagnostic system to characterize CT focal liver lesions: design and optimization of a neural network classifier. IEEE Transact. on Information Technol. Biomed. 7(3). 153-162.
  • Geetha K and Thanushkodi K (2008) Particle Swarm Algorithm for automatic Detection in Breast cancer. Intl. J. Soft Comput. 3(2). 155-158.
  • Gerald Schaefer, Tomoharu Nakashima, Michal Zaivi-sek, Yasuyuki Yokota, Ale-s Drastich and Hisao Ishibuchi (2007) Breast cancer classification using statistical features and fuzzy classification of thermograms. IEEE Conf. on Fuzzy Sys. pp:1-5.
  • Guo-Zheng Li, Jie Yang, Chen-Zhou Ye and Dao- Ying Geng (2006) Degree prediction of malignancy in brain glioma using support vector machines. J. Comput. Biol. & Med. 36(3), 313-325.
  • Georgia D Tourassi, Mia K Markey, Joseph Y Lo and Carey E Floyd Jr (2001) A neural network approach to breast cancer – diagnosis as a constraint satisfaction problem. J. American Associ. Phys. Medicine. 28(5), 804-811.
  • Hong J and Cho S (2006) Efficient huge scale feature selection with speciated genetic algorithm. J. Pattern Recognition Letters. 27,143-150.
  • Ireaneus Anna Rejani Y and Thamarai Selvi S (2009) Early detection of breast cancer using SVM classifier technique. Intl. J. Compu. Sci. & Engg. 1(3), 127-130.
  • lvarez A, Gorriz JM, Ramırez J, Salas-Gonzalez D, Lopez M, Puntonet CG and F Segovia(2009) Alzheimer’s diagnosis using eigenbrains and support vector machines. Electronics Letters ,45(7), 342-343.
  • Isabelle Guyon, Jason Weston, Stephen Barnhill and Vladimir Vapnik (2002) Gene selection for cancer classification using support vector machines. J. Machine Learning. pp: 389-422.
  • Ilias Maglogiannis, Elias Zafiropoulos and Christos Kyranoudis (2006) Intelligent segmentation and classification of pigmented skin lesions in dermatological images. Advances in Artificial Intelligence. 3955. 214-223.
  • Jinn-Yi Yeh and J C Fu (2008) A hierarchical genetic algorithm for segmentation of multi-spectral humanbrain MRI. Expert sys. with Appli. 34(2), 1285-1295.
  • Jan Luts , Arend Heerschap , Johan A K Suykens ,Sabine Van Huffel (2007) A combined MRI and MRSI based multiclass system for brain tumour recognition using LS-SVMs with class probabilities and feature selection. J. Artificial Intelligence in Medicine. 40(2), 87-102.
  • Jiawan Zhang and Jizhou Sun (2004) Automatic Classification of MRI Images for Three-dimensional Volume Reconstruction by Using General Regression Neural Networks. IEEE Conf. on Nuclear Sci. 5, 3188-3189.
  • Jiang Y, Nishiikawa RM, Schmidt RA, Metz CE, Giger ML and Doi K (1999) Improving breast cancer diagnosis with computer aided diagnosis. J. Acad. Radiol. 6(1), 22-33.
  • Jude Hemanth D, Kezi Selva Vijila C and Anitha J (2010) Performance improved PSO based modified counter propagation neural network for abnormal MR brain image classification. Int. J. Advance. Soft Comput. Appl. 2(1), 65-84.
  • Karol Przystalski, Leszek Nowak, Maciej Ogorzałek and Grzegorz Surówka (2010) Decision support system for skin cancer diagnosis. Intl. Sym. Operations Res. & Appl. pp: 406–413.
  • Katharina Völk, Julian F Miller and Stephen L Smith (2009) Multiple network CGP for the classification of mammograms. Evolutionary Workshop ’09, Springer- Verlag. pp:405-413.
  • Kang H, Pinti A, Taleb-Ahmed A and X Zeng (2011) An intelligent generalized system for tissue classification on MR images by integrating qualitative medical knowledge. J. Biomed. Signal Processing & Control. 6, 21–26.
  • Kishore JK, Lalit M Patnaik, Mani V and Agrawal VK (2009) Application of genetic programming for multicategory pattern classification. IEEE Trans. Evolutionary Computation, 4, 242-258.
  • Khazaee and Ebrahimzadeh A (2010) Classification of electrocardiogram signals with support vector machines and genetic algorithms using power spectral features. J. Biomedical Signal Processing & Control. 5, 252–263.
  • Kumar SS and Moni RS (2010) Diagnosis of liver tumor from CT images using fast discrete curvelet transform. Intl. J. Comput. Appl. Special Issue on CASCT. 1, 1–6.
  • Latha Parthiban and Subramanian R (2007) Intelligent heart disease prediction system using CANFIS and genetic algorithm. Intl. J. Biological & Life Sci. 3,157-160.
  • Liang CE (1998) An automatic diagnostic system for CT liver image classification. IEEE Trans. Biom. Engg. 45(6), 783-794.
  • Leonardo de Oliveira Martins, Aristofanes Correa Silva, Anselmo Cardoso de Paiva and Marcelo Gattass (2009) Detection of breast masses in mammogram images using growing neural gas algorithm and ripley’s K function. J. Signal Processing Sys. 55, 77-90.
  • Leena Jasmine JS, Govardhan A and Baskaran S (2010) Classification of microcalcification in mammograms using non subsampled contourlet transform and neural network. Eur. J. Scientific Res. 46(4), 531-539.
  • Mueen A, Sapiyan Baba M and Zainuddin R (2007) Multilevel feature extraction and X-ray classification. J. Applied Sci. 7(8), 1224-1229.
  • Madhubanti Maitra and Amitava Chatterjee (2008) A novel technique for multilevel optimal magnetic resonance brain image thresholding using bacterial foraging. J. Measurement. 41(10), 1124-1134.
  • Mougiakakou, Valavanis SG, Nikita I, Nikita KS and Kelekis AD (2003) Characterization of CT liver lesions based on texture features and a multiple neural network classification scheme. Proce. 25th Annual Intl. Conf. the IEEE in Med. & Biol. Soci. pp:1287-1290.
  • Matteo Masotti (2006) A ranklet –based image representation for mass classification in digital mammograms. J. Medical Phys. 33(10), 3951-3961.
  • Mohammed J Islam, Majid Ahmadi and Maher A Sid- Ahmed (2010) An efficient automatic mass classification method in digitized mammograms using artificial neural network. Intl. J. Artificial Intelligence & Appl. 1(3),1-13.
  • Nakamoto T, Taguchi A, Ohtsuka M, Suei Y, FujitaM, Tsuda M, Sanada M, Kudo Y, Asano A and K Tanimot (2008) A computer-aided diagnosis system to screen for osteoporosis using dental panoramic radiographs. J. Dentomaxillofacial Radiol. 37(5), 274–281.
  • Nandi RJ, Nandi AK, Rangayyan RM and Scutt D (2006) Classification of breast masses in mammograms using genetic programming and feature selection. J. Medical & Biological Engg. Computation. 44, 683-694.
  • Niyazi Kilic, Pelin Gorgel, Osman N Ucan and Ahmet Sertbas (2010) Mammographic mass detection using wavelets as input to neural networks. J. Med. Sys. 34(6), 1083-1088.
  • Nosratallah Forghani, Mohamad Forouzanfar and Elham Fourouzanfar (2007) MRI fuzzy segmentation of brain tissue using IFCM Algorithm with particle swarm optimization. IEEE Intl. Sym. Comput. & information sci. pp:113-121.
  • Pietro Rubegni, Gabriele Cevenini, Marco Burroni, Roberto Perotti, Giordana Dell’Eva, Paolo Sbano, Clelia Miracco, Pietro Luzi, Piero Tosi, Paolo Barbini and Lucio Andreass (2002) Automated diagnosis of pigmented skin lesions. Intl. J. Cancer. 101(6), 576-580.
  • Rahib H Abiyev and Koray Altunkaya (2008) Personal iris recognition using neural network. Intl. J. Security & its Appl. 2(2), 41-50.
  • Ramakrishnan S and Selvan S (2006) Classification of brain tissues using multiwavelet transformation and probabilistic neural network. Intl. J. Simulation: Sys. Sci. & Technol. 7(9), 9–25.
  • Ramana KV and Raghu B Korrapati (2010) Neura network based classification and diagnosis of brain hemorrhages. Intl. J. Artificial Intelligence & Expert Sys. 1(2), 7-25.
  • Ramakrishnan S, Ibrahiem El and Emary MM (2010) Classification brain MR images through a fuzzy multiwavelets based GMM and probabilistic neural networks. J. Telecom. Sys. Springer Sci. 46(3), 245-252.
  • Revathy N and R Amalraj (2011) Accurate cancer classification using expressions of very few genes. Intl. J. Comput. Appli. 14(4),19–22.
  • Riyahi Alam N, Younesi F and Riyahi Alam MS (2009) Computer-Aided mass detection on digitized mammograms using a novel hybrid segmentation system. Intl. J. Biol. & Biomedical Engg. 3(4), 51-58.
  • Rinku Panchal and Brijesh Verma (2004) A Fusion of Neural Network Based Auto-associator and Classifier for the Classification of Microcalcification Patterns. Intl. Conf. Neural Information Processing, Springer Berlin. pp:794-799.
  • Rolando R Hernandez-Cisneros and Hugo Terashima-Marın (2010) Classification of individual and clustered microcalcifications in digital mammograms using evolutionary neural networks. Mexican Intl. Conf. Artificial Intelligence. pp: 1200–1210.
  • Saha S and Bandyopadhyay S (2007) MRI brain image segmentation by fuzzy symmetry based genetic clustering technique. IEEE Congress on Evolutionary Computation. pp: 4417 – 4424.
  • Saras Saraswathi, Suresh Sundaram, Narasimhan Sundararajan, Michael Zimmermann and Marit Nilsen Hamilton (2011) ICGA- PSO-ELM Approach for accurate multiclass cancer classification resulting in reduced gene sets in which genes encoding secreted proteins are highly represented. IEEE/ACM Transactions on Computational Biol. & Bioinfo. 8(2), 452-463.
  • Shafaf Ibrahim and Noor Elaiza Abdul (2010) Empirical study of brain segmentation using particle swarm optimization. IEEE Intl. Conf. on Information Retrieval & Knowledge Managt. pp:235-239.
  • Shao Hong, Ni Tian-yu, Kang Yan and Zhao Hong (2010) Chest DR image classification based on support vector machine. IEEE Intl. Workshop on Education Technol. & Comput. Sci. 170-173.
  • Saheb Basha S and Satya Prasad K (2009) Automatic detection of breast cancer mass in mammograms using morphological operators and Fuzzy C- means clustering. J. Theoretical & Appl. Information Technol. 5(6), 704-709.
  • Sang-Hyun Hwang, Dongwon Kim, Tae-Koo Kang and Gwi-Tae Park (2007) Medical diagnosis system of breast cancer using FCM based parallel neural networks. Intl. Conf. Intelligent Comput. Springer. pp:712-719.
  • Selvaraj H, Thamarai Selvi S, Selvathi D and Gewali L (2007) Brain MRI slices classification using least squares support vector machine. Intl. J. Intelligent Comput. in Medical Sci. & Image Processing. 1(1), 21- 33.
  • Sathish Chandra, Rajesh Bhat, Harinder Singh and Chauhan DS (2009) Detection of brain tumors from MRI using gaussian RBF kernel based support vector machine. Intl. J. Digital Content Technol. & its Appli. 1(9), 46-51.
  • Sepehr MH Jamarani, Behnam H and Rezai rad GA (2005) Multiwavelet based neural network for breast cancer diagnosis. Intl. Conf. Graphics, Vision & Image Processing, Egypt. pp:29-34.
  • Stuart Russell and Peter Norvig (2002) Artificial Intelligence: A modern approach (Second Edition), Prentice Hall.
  • Vassilis S Kodogiannis and John N Lygouras (2008) Neuro-fuzzy classification system for wirelesscapsule endoscopic images. J. World Acad. Sci. Engg. & Technol., 45, 620-628.
  • Vasantha M and Subbiah Bharathi V and Dhamodharan R (2010) Medical image feature, extraction, selection and classification. Intl. J. Engg. Sci. & Technol. 2(6), 2071-2076.
  • Wang P, Krishnan SM, Kugean C and Tjoa MP (2001) Classification of endoscopic images based on texture and neural network. Proc. 23rd Annual EMBS Intl. Conf. pp:3691-3695.
  • Xiaogang Ruan, Jinlian Wang, Hui Li and Xiaoming Li (2008) A method for cancer classification using ensemble neural networks with gene expression profile. IEEE Conf. Bioinfor. & Biomed. Engg. pp:342-346.
  • Ye CZ, Yang J, Geng DY, Zhou Y and Chen NY (2002) Fuzzy rules to predict degree of malignancy in brain glioma. J. Med. Biol. Engg. Computation. 40, 145-152.
  • YuZhang, YuZhang, Xiaopeng Xie and Taobo Cheng (2001) Application of PSO and SVM in Image Classification. IEEE Intl. Conf. Compu. Sci. & Information Technol. pp:629-631.
  • Yuehui Chen, Yan Wang and Bo Yang (2006) Evolving hierarchical RBF Neural networks for breast cancer detection. Intl. Conf. Neural Information Processing, Springer-Berlin. pp:137-144.
  • Zhou Xian-cheng, Shen Qun-tai and Liu Li-mei (2008) New two-dimensional fuzzy C-means clustering algorithm for image segmentation. J. Central South Univ. Technol. 15(6), 882-887.

Abstract Views: 1002

PDF Views: 345




  • A Survey on Artificial Intelligence Approaches for Medical Image Classification

Abstract Views: 1002  |  PDF Views: 345

Authors

S. N. Deepa
Department of EEE, Anna University of Technology, Coimbatore-641047, India
B. Aruna Devi
Department of EEE, Anna University of Technology, Coimbatore-641047, India

Abstract


In this paper, a survey has been made on the applications of intelligent computing techniques for diagnostic sciences in biomedical image classification. Several state-of-the-art Artificial Intelligence (AI) techniques for automation of biomedical image classification are investigated. This study gathers representative works that exhibit how AI is applied to the solution of very different problems related to different diagnostic science analysis. It also detects the methods of artificial intelligence that are used frequently together to solve the special problems of medicine. SVM neural network is used in almost all imaging modalities of medical image classification. Similarly fuzzy C means and improvements to it are important tool in segmentation of brain images. Various diagnostic studies like mammogram analysis, MRI brain analysis, bone and retinal analysis etc., using neural network approach result in use of back propagation network, probabilistic neural network, and extreme learning machine recurrently. Hybrid approach of GA and PSO are also commonly used for feature extraction and feature selection.

Keywords


Medical Imaging, Artificial Intelligence (AI), Neural Networks (NN), Fuzzy Logic (FL), Genetic Algorithms (GA), Particle Swarm Optimization (PSO)

References





DOI: https://doi.org/10.17485/ijst%2F2011%2Fv4i11%2F30291