Pap smear image classification using convolutional neural network

ACM International Conference Proceeding Series

Volumn , Issue , 2016, Pages

  Bora, Kangkana ^a   Chowdhury, Manish ^b   Mahanta, Lipi B ^a   Kundu, Malay K ^c   Das, Anup K ^d  

^a INSTITUTE OF ADVANCED STUDY IN SCIENCE AND TECHNOLOGY   (India)

^b ROYAL INSTITUTE OF TECHNOLOGY   (Sweden)

^c INDIAN STATISTICAL INSTITUTE   (India)

^d Ayursundra Healthcare Pvt Ltd Ltd   (India)

Author keywords

Deep learning; LSSVM; Pap smear image; Softmax regression

Indexed keywords

COMPUTER AIDED INSTRUCTION; COMPUTER VISION; CONVOLUTION; DEEP LEARNING; DEEP NEURAL NETWORKS; IMAGE CLASSIFICATION; IMAGE RETRIEVAL; NEURAL NETWORKS; SUPPORT VECTOR MACHINES;

COMPUTER AIDED DIAGNOSTICS; CONVOLUTIONAL NEURAL NETWORK; CROSS-VALIDATION TECHNIQUE; LEAST SQUARE SUPPORT VECTOR MACHINES; LSSVM; PAP SMEAR IMAGES; PAP-SMEAR CLASSIFICATION; SOFTMAX REGRESSIONS;

IMAGE PROCESSING;

EID: 85014841160     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/3009977.3010068     Document Type: Conference Paper

References (27)

1. Y. Bar, I. Diamant, L. Wolf, S. Lieberman, E. Konen, and H. Greenspan. Chest pathology detection using deep learning with non-medical training. In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pages 294-297, 2015.
2. L. H. Camargo, G. Diaz, and E. Romero. Pap smear cell image classification using global mpeg7 descriptor. Dignostic Pathology, 8 ( (Suppl 1): S38): 1-4, 2013.
3. R. A. Castellino. Computer aided detection (cad): An overview. Cancer Imaging, 5 (1): 17-19, 2005.
4. T. Chankong, N. T. Umpon, and S. Auephanwiriyankul. Automatic cervical cell segmentation and classification in pap smears. Computer Methods and Programs in Biomedicine, 113 (2): 539-556, 2014.
5. J. M. Chen, A. P. Qu, L. W. Wang, J. P. Yuan, F. Yang, X. Q. Ming, N. Maskey, G. F. Yang, J. Liu, and Y. Li. New breast cancer prognostic factor identified by computer aided image analysis of he stanined histopathology images. Scientific Reports, 5: 1-13, 2015.
6. J. Z. Cheng, D. Ni, J. Qin, C. M. Tiu, Y. C. Chang, C. S. Huang, S. Dinggang, and C. M. Chen. Computer-aided diagnosis with deep learning architecture: Applications to breast lesions in us images and pulmonary nodules in ct scans. Scientific Reports, 6: 1-13, 2016.
7. M. Chowdhury and M. K. Kundu. Comparative assessment of efficiency for content based image retrieval systems using different wavelet features and pre-classifier. Multimedia Tools and Application, 74 (24): 11595-11630, 2015.
8. A. Genctav, S. Aksoy, and S. Onder. Unsupervised segmentation and classification of cervical cell images. Pattern Recognition, 45 (12): 4151-4168, 2012.
9. W. Gray. Diagnostic Cytopathology. Churchill Livingstone, 2010.
10. D. Gujarati. Econometrics by Example. Palgrave, 2012.
11. C. W. Hsu and C. J. Lin. A comparison of methods for multi-class support vector machines. Neural Network, IEEE transaction on, 13 (2): 427-437, 2002.
12. J. Jantzen, J. Norup, G. Dounias, and B. Bjerregaard. Pap-smear benchmark data for pattern classification. In Proc. NiSIS 2005, pages 1-9. NiSIS, 2005.
13. Y. Jia, E. Shelhamer, J. Donahue, and S. Karayev. Caffe: Convolutional architecture for fast feature embedding. In In Proc. of MM, page 675-678, 2016.
14. P. Kontschieder, M. Fiterau, A. Criminisi, and S. R. Bulo. Deep neural decision forest. In Proceeding of ICCV, pages 1467-1475, 2015.
15. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems 25 (NIPS 2012), pages 1097-1105, YEAR =2012.
16. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural network. In Proceeding of NIPS, pages 1106-1114, 2015.
17. N. Lassouaoui, L. Hamami, and N. Nouali. Morphological description of cervical cell images for the pathological recognition. International Journal of Medical, Health, Pharmaceutical and Biomedical Engineering, 1 (5): 307-310, 2007.
18. Y. LeCun, Y. Bengioand, and G. E. Hinton. Deep learning. Nature, 521: 436-444, 2015.
19. Z. Lu, G. Carneiro, and A. P. Bardley. An improved joint optimization of multiple level set function for the segmentation of overlapping cervical cells. Image Processing, IEEE Transaction on, 24 (4): 1261-1272, 2015.
20. P. Mitra, C. A. Murthy, and S. K. Pal. Unsupervised feature selection using feature similarity. Pattern analysis and Machine Inteligence, IEEE Transaction on, 24 (3): 301-312, 2002.
21. M. E. Plissiti and C. Nikou. Combining shape, texture and intensity features for cell nuclei extraction in pap smear images. Pattern Recognition Letters, 32 (6): 838-853, 2011.
22. M. E. Plissiti, C. Nikou, and A. Charchanti. Watershed-based segmentation of cell nuclei boundaries in pap smear images. In Information Technology and Applications in Biomedicine (ITAB), 10th IEEE International Conference on, pages 1-4. IEEE, 2010.
23. R. Samala, H. P. Chan, L. Hadjiiski, and M. Helvie. Deep-learning convolution neural network for computer-aided detection of microcalcifications in digital breast tomosynthesis. In SPIE Medical Imaging, Volume: 9785, 2016.
24. P. Seebock. Deep learning in medical image analysis. Master's thesis, Vienna University of Technology, Faculty of Informatics, 2015.
25. H. Shin, H. Roth, M. Chen, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. Summers. Deep convolutional neural networks for computer-aided detection: Cnn architectures, dataset characteristics and transfer learning. Medical Imaging, IEEE Transaction on, 2016.
26. M. Sokolova and G. Lapalme. A systematic analysis of performance measure for classification task. Information Processing and Management, 45 (4): 427-437, 2009.
27. J. A. K. Suykens and J. Vandewalle. Least squares support vector machine classifiers. Neural Processing Letters, 9 (3): 293-300, 1999.