Journal of Engineering and Applied Sciences

Year: 2018
Volume: 13
Issue: 3 SI
Page No. 3152 - 3158

A Review: Supervised Technique for Automated Disease Diagnostic Using Medical Image

Authors : Mazniha Berahim, Noor Azah Samsudin and Shelena Soosay Nathan

References

Aalaei, S., H. Shahraki, A. Rowhanimanesh and S. Eslami, 2016. Feature selection using genetic algorithm for breast cancer diagnosis: Experiment on three different datasets. Iran. J. Basic Med. Sci., 19: 476-482.
PubMed  |  

Agrawal, S. and J. Agrawal, 2015. Neural network techniques for cancer prediction: A survey. Procedia Comput. Sci., 60: 769-774.
CrossRef  |  Direct Link  |  

Akinola, S.O. and O.J. Oyabugbe, 2015. Accuracies and training times of data mining classification algorithms: An empirical comparative study. J. Software Eng. Appl., 8: 470-477.
Direct Link  |  

Akram, M.U., S. Khalid and S.A. Khan, 2013. Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recognit., 46: 107-116.
CrossRef  |  Direct Link  |  

Amato, F., A. Lopez, P.E.M. Mendez, P. Vanhara and A. Hampl and J. Havel, 2013. Artificial neural networks in medical diagnosis. J. Appl. Biomed., 11: 47-58.

Angayarkanni, A.S.P. and B.N.B. Kamal, 2012. Automatic classification of mammogram MRI using dendograms. Asian J. Comput. Sci. Inf. Technol. J., 4: 78-81.

Athinarayanan, S. and M.V. Srinath, 2016. Classification of cervical cancer cells in PAP smear screening test. ICTACT. J. Image Video Processing, 6: 1234-1237.
CrossRef  |  Direct Link  |  

Avula, M., N.P. Lakkakula and M.P. Raja, 2014. Bone cancer detection from MRI scan imagery using mean pixel intensity. Proceedings of the 2014 8th International Conference on Asia Modelling Symposium (AMS), September 23-25, 2014, IEEE, Hyderabad, India, ISBN:978-1-4799-6487-1, pp: 141-146.

Battula, B.P. and R.S. Prasad, 2013. An overview of recent machine learning strategies in data mining. Intl. J. Adv. Comput. Sci. Appl., 4: 50-54.
Direct Link  |  

Beheshti, S.M.A., H.A. Noubari, E. Fatemizadeh and M. Khalili, 2016. Classification of abnormalities in mammograms by new asymmetric fractal features. Biocybernetics Biomed. Eng., 36: 56-65.
Direct Link  |  

Beniwal, S. and J. Arora, 2012. Classification and feature selection techniques in data mining. Intl. J. Eng. Res. Technol., 1: 1-6.

Bettencourt, L.A. and S.W. Brown, 2003. Role stressors and customer-oriented boundary-spanning behaviors in service organizations. J. Acad. Market. Sci., 31: 394-408.
Direct Link  |  

Carrobles, F.M.M., G. Bueno, O. Deniz, J. Salido and R.M. Garcia et al., 2015. Frequential versus spatial colour textons for breast TMA classification. Computerized Med. Imaging Graphics, 42: 25-37.
PubMed  |  Direct Link  |  

Chitra, R. and V. Seenivasagam, 2013. Review of heart disease prediction system using data mining and hybrid intelligent techniques. ICTACT. J. Soft Comput., 3: 605-609.

Cordeiro, F.R., W.P. Santos and F.A.G. Silva, 2016. An adaptive semi-supervised fuzzy GrowCut algorithm to segment masses of regions of interest of mammographic images. Appl. Soft Comput., 46: 613-628.
Direct Link  |  

Das, S., M. Chowdhury and M.K. Kundu, 2013. Brain MR image classification using multiscale geometric analysis of ripplet. Prog. Electromagnet. Res., 134: 1-17.
Direct Link  |  

Decenciere, E., G. Cazuguel, X. Zhang, G. Thibault and J.C. Klein et al., 2013. TeleOphta: Machine learning and image processing methods for teleophthalmology. IRBM., 34: 196-203.
Direct Link  |  

Doukim, C., J. Dargham and A. Chekima, 2014. State of the art of content-based image classification. Proceedings of the 2014 International Conference on Computational Science and Technology (ICCST), August 27-28, 2014, IEEE, Kota Kinabalu, Malaysia, ISBN:978-1-4799-3241-2, pp: 1-6.

Fenwa, O.D., F.A. Ajala and A.M. Aku, 2015. Performance evaluation of support vector machine and artificial neural network in the classification of liver cirhosis and hemachromatosis. Proceedings of the 2015 International Conference on Computer Vision and Image Analysis Applications (ICCVIA), January 18-20, 2015, IEEE, Ogbomoso, Nigeria, ISBN:978-1-4799-7186-2, pp: 1-6.

Fu, J.J., Y.W. Yu, H.M. Lin, J.W. Chai and C.C.C. Chen, 2014. Feature extraction and pattern classification of colorectal polyps in colonoscopic imaging. Computerized Med. Imaging Graphics, 38: 267-275.
PubMed  |  Direct Link  |  

Ganesan, K., R.U. Acharya, C.K. Chua, L.C. Min and B. Mathew et al., 2013. Decision support system for breast cancer detection using mammograms. Proc. Inst. Mech. Eng. Part H. J. Eng. Med., 227: 721-732.
Direct Link  |  

Ghadge, P., V. Girme, K. Kokane and P. Deshmukh, 2016. Intelligent heart attack prediction system using big data. Intl. J. Recen Res. Math. Comput. Sci. Inf. Technol., 2: 73-77.

Ghosh, S., S. Mondal and B. Ghosh, 2014. A comparative study of breast cancer detection based on SVM and MLP BPN classifier. Proceedings of the 2014 1st International Conference on Automation, Control, Energy and Systems (ACES), February 1-2, 2014, IEEE, Hooghly, India, ISBN:978-1-4799-3894-0, pp: 1-4.

Hemanth, D.J., C.K.S. Vijila, A.I. Selvakumar and J. Anitha, 2014. Performance improved iteration-free artificial neural networks for abnormal magnetic resonance brain image classification. Neurocomputing, 130: 98-107.
Direct Link  |  

Hiremath, B. and S. Prasannakumar, 2015. Automated evaluation of breast cancer detection using svm classifier. Intl. J. Comput. Sci. Eng. Inf. Technol. Res., 5: 11-20.

Huber, M.B., K. Bunte, M.B. Nagarajan, M. Biehl and L.A. Ray et al., 2012. Texture feature ranking with relevance learning to classify interstitial lung disease patterns. Artif. Intell. Med., 56: 91-97.
Direct Link  |  

James, A.P. and B.V. Dasarathy, 2014. Medical image fusion: A survey of the state of the art. Inf. Fusion, 19: 4-19.
CrossRef  |  Direct Link  |  

Jarrah, A.O.Y., P.D. Yoo, S. Muhaidat, G.K. Karagiannidis and K. Taha, 2015. Efficient machine learning for big data: A review. Big Data Res., 2: 87-93.
Direct Link  |  

Kadi, A.O.S., 2015. A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours. Computerized Med. Imaging Graphics, 41: 67-79.
PubMed  |  Direct Link  |  

Khan, A., N.A. Syed and M. Reyaz, 2015. Image processing techniques for brain tumor extraction from MRI images using SVM classifier. Int. J. Recent Innov. Trends Comput. Commun., 3: 2707-2711.
Direct Link  |  

Kharya, S., 2012. Using data mining techniques for diagnosis and prognosis of cancer disease. Int. J. Comput. Sci. Eng. Inf. Technol., 2: 55-66.
CrossRef  |  Direct Link  |  

Krawczyk, B., M. Galar, L. Jelen and F. Herrera, 2016. Evolutionary undersampling boosting for imbalanced classification of breast cancer malignancy. Appl. Soft Comput., 38: 714-726.
Direct Link  |  

Kumar, A. and N. Kannathasan, 2011. A survey on data mining and pattern recognition techniques for soil data mining. Intl. J. Comput. Sci., 8: 422-428.
Direct Link  |  

Kumar, Yugal and G. Sahoo, 2012. Analysis of Parametric & Non Parametric Classifiers for Classification Technique using WEKA. Int. J. Inf. Technol. Comput. Sci., 4: 43-49.
Direct Link  |  

Lashari S.A. and R. Ibrahim, 2013. Comparative analysis of data mining techniques for medical data classification. Proceedings of the 4th International Conference on Computer Informatics Vol. 4, August 28-30, 2013, Universiti Utara Malaysia, Changlun, Malaysia, ISBN:9789832078791, pp: 365-370.

Lashari, S.A. and R. Ibrahim, 2013. A framework for medical image classification using soft set. Procedia Technol., 8: 537-545.
Direct Link  |  

Legg, P.A., P.L. Rosin, D. Marshall and J.E. Morgan, 2015. Feature neighbourhood mutual information for multi-modal image registration: An application to eye fundus imaging. Pattern Recognit., 48: 1937-1946.
Direct Link  |  

Liberman, G., Y. Louzoun, O. Aizenstein, D.T. Blumenthal and F. Bokstein et al., 2013. Automatic multi-modal MR tissue classification for the assessment of response to bevacizumab in patients with glioblastoma. European. J. Radiol., 82: e87-e94.
CrossRef  |  Direct Link  |  

Liu, D., S. Wang, D. Huang, G. Deng and F. Zeng et al., 2016. Medical image classification using spatial adjacent histogram based on adaptive local binary patterns. Comput. Biol. Med., 72: 185-200.
PubMed  |  Direct Link  |  

Liu, L. and L. Wang, 2014. HEp-2 cell image classification with multiple linear descriptors. Pattern Recognit., 47: 2400-2408.
Direct Link  |  

Liu, S., W. Cai, S. Liu, S. Pujol and R. Kikinis et al., 2015. Subject-centered multi-view feature fusion for neuroimaging retrieval and classification. Proceedings of the 2015 IEEE International Conference on Image Processing, September 27-30, 2015, IEEE, Sydney, Australia, ISBN:978-1-4799-8339-1, pp: 2505-2509.

Mala, K., V. Sadasivam and S. Alagappan, 2015. Neural network based texture analysis of CT images for fatty and cirrhosis liver classification. Appl. Soft Comput., 32: 80-86.
Direct Link  |  

Mbaga, A.H. and P. ZhiJun, 2015. Pap smear images classification for early detection of cervical cancer. Intl. J. Comput. Appl., 118: 7-16.
Direct Link  |  

Nadiri, H. and C. Tanova, 2010. An investigation of the role of justice in turnover intentions, job satisfaction and organizational citizenship behavior in hospitality industry. Int. J. Hospitality Manage., 29: 33-41.
CrossRef  |  

Nasser, A.M., H.A. Rashwan, D. Puig and A. Moreno, 2015. Analysis of tissue abnormality and breast density in mammographic images using a uniform local directional pattern. Expert Syst. Appl., 42: 9499-9511.
Direct Link  |  

Ota, K., N. Oishi, K. Ito, H. Fukuyama and J. Sead et al., 2015. Effects of imaging modalities, brain atlases and feature selection on prediction of Alzheimer's disease. J. Neurosci. Methods, 256: 168-183.
PubMed  |  Direct Link  |  

Owolabi, A.B., 2012. Effect of organizational justice and organizational environment on turn-over intention of health workers in Ekiti state, Nigeria. Res. World Econ., 3: 28-34.
Direct Link  |  

Rouhi, R. and M. Jafari, 2016. Classification of benign and malignant breast tumors based on hybrid level set segmentation. Expert Syst. Appl., 46: 45-59.
Direct Link  |  

Saaty, T. and L. Vargas, 1987. Uncertainty and rank ordering in the analytic hierarchy process. Eur. J. Operational Res., 32: 102-118.

Sahu, P. and A.K. Jain, 2014. A review on new data mining techniques for x-ray image classification. Intl. J. Adv. Technol. Eng. Res., 1: 55-60.

Schwenker, F. and E. Trentin, 2014. Pattern classification and clustering: A review of partially supervised learning approaches. Pattern Recognit. Lett., 37: 4-14.
Direct Link  |  

Seetharaman, K. and S. Sathiamoorthy, 2016. A unified learning framework for content based medical image retrieval using a statistical model. J. King Saud Univ. Comput. Inf. Sci., 28: 110-124.
Direct Link  |  

Sharif, M.S., R. Qahwaji, S. Ipson and A. Brahma, 2015. Medical image classification based on artificial intelligence approaches: A practical study on normal and abnormal confocal corneal images. Appl. Soft Comput., 36: 269-282.
Direct Link  |  

Silverthorne, C., 2004. The impact of organizational culture and person-organization fit on organizational commitment and job satisfaction in Taiwan. Leadersh. Organiz. Dev. J., 25: 592-599.
CrossRef  |  Direct Link  |  

Singh, B.K., K. Verma and A.S. Thoke, 2015. Adaptive gradient descent backpropagation for classification of breast tumors in ultrasound imaging. Procedia Comput. Sci., 46: 1601-1609.
Direct Link  |  

Smitha, P., L. Shaji and M.G. Mini, 2011. A review of medical image classification techniques. Proceedings of the International Conference on VLSI, Communications and Instrumentation, Volume 11, April 7-9, 2011, Kottayam, India, pp: 34-38.

Stoklasa, R., T. Majtner and D. Svoboda, 2014. Efficient K-NN based HEp-2 cells classifier. Pattern Recognit., 47: 2409-2418.
Direct Link  |  

Sudhakar, K. and D.M. Manimekalai, 2014. Study of heart disease prediction using data mining. Intl. J. Adv. Res. Comput. Sci. Software Eng., 4: 1157-1160.
Direct Link  |  

Thamilselvan, P. and J. Sathiaseelan, 2016. An enhanced k nearest neighbor method to detecting and classifying MRI lung cancer images for large amount data. Int. J. Appl. Eng. Res., 11: 4223-4229.
Direct Link  |  

Thamilselvan, P. and J.G.R. Sathiaseelan, 2015. Image classification using hybrid data mining algorithms-a review. Proceedings of the 2015 International Conference on Innovations in Information, Embedded and Communication Systems, March 19-20, 2015, IEEE, Tamilnadu, India, ISBN:978-1-4799-6818-3, pp: 1-6.

Thepade, S.D. and M.M. Kalbhor, 2015. Novel data mining based image classification with Bayes, tree, rule, lazy and function classifiers using fractional row mean of cosine, sine and walsh column transformed images. Proceedings of the 2015 International Conference on Communication, Information and Computing Technology (ICCICT), January 15-17, 2015, IEEE, Pune, India, ISBN:978-1-4799-5522-0, pp: 1-6.

Virmani, J., V. Kumar, N. Kalra and N. Khadelwal, 2011. A rapid approach for prediction of liver cirrhosis based on first order statistics. Proceedings of the 2011 International Conference on Multimedia, Signal Processing and Communication Technologies, December 17-19, 2011, IEEE, Roorkee, India, ISBN:978-1-4577-1107-7, pp: 212-215.

Wang, N., J. Li, D. Tao, X. Li and X. Gao, 2012. Heterogeneous image transformation. Pattern Recognit. Lett., 34: 77-84.
CrossRef  |  

Wang, S. and R.M. Summers, 2012. Machine learning and radiology. Med. Image Anal., 16: 933-951.
Direct Link  |  

Xiang, Z., X. Lv and K. Zhang, 2014. An image classification method based on multi-feature fusion and multi-kernel SVM. Proceedings of the 2014 7th International Symposium on Computational Intelligence and Design Vol. 2, December 13-14, 2014, IEEE, Beijing, China, ISBN:978-1-4799-7005-6, pp: 49-52.

Xie, W., Y. Li and Y. Ma, 2016. Breast mass classification in digital mammography based on extreme learning machine. Neurocomputing, 173: 930-941.
Direct Link  |  

Zare, M.R., A. Mueen and W.C. Seng, 2013. Automatic classification of medical X-ray images using a bag of visual words. IET Comput. Vision, 7: 105-114.
CrossRef  |  

Zhang, Y. and L. Wu, 2012. An MR brain images classifier via principal component analysis and kernel support vector machine. Prog. Electromagnet. Res., 130: 369-388.
PubMed  |  Direct Link  |  

Zhang, Y., S. Wang, G. Ji and Z. Dong, 2013. An MR brain images classifier system via particle swarm optimization and kernel support vector machine. Sci. World J., 2013: 1-9.
Direct Link  |  

Design and power by Medwell Web Development Team. © Medwell Publishing 2024 All Rights Reserved