Comparative assessment of CNN architectures for classification of breast FNAC images

Tissue and Cell - Tập 57 - Trang 8-14 - 2019
Amartya Ranjan Saikia1, Kangkana Bora2, Lipi B. Mahanta2, Anup Das3
1The Department of Computer Science and Engineering, Assam Engineering College, Guwahati 781013, Assam, India
2The Department of Centre for Computational and Numerical Sciences, Institute of Advanced Study in Science and Technology, Guwahati 781035, Assam, India
3Arya Wellness Center, Guwahati 781032, Assam, India

Tóm tắt

Từ khóa


Tài liệu tham khảo

Bora, 2016, Pap smear image classification using convolutional neural network, ACM International Conference Proceeding Series, ICVGIP, 18–22 December 2016, IIT Guwahati

U.S. Breast Cancer Statistics, Technical Report, https://www.breastcancer.org/symptoms/understand_bc/statistics.

Das, 2017, Classifying histopathology whole-slides using fusion of decisions from deep convolutional network on a collection of random multi-views at multi-magnification, IEEE International Symposium on Biomedical Imaging

Demir, 2005

Doreswamy, 2015, Fast modular artificial neural network for the classification of breast cancer data, Proceedings of the Third International Symposium on Women in Computing and Informatics, 66, 10.1145/2791405.2791535

Ducatman, 2009, Chapter 8 – Breast, 221

Garud, 2012, Breast fine needle aspiration cytology practices and commonly perceived diagnostic significance of cytological features: a pan-India survey, J. Cytol., 293, 183, 10.4103/0970-9371.101168

Garud, 2017, Methods and system for segmentation of isolated nuclei in microscopic images of breast fine needle aspiration cytology images., IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

Garud, 2017, High-magnification multi-views based classification of breast fine needle aspiration cytology cell samples using fusion of decisions from deep convolutional networks, IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 828

Havaei, 2017, Brain tumor segmentation with deep neural networks, Med. Image Anal., 35, 18, 10.1016/j.media.2016.05.004

He, 2016, Deep residual learning for image recognition, IEEE Conference on Computer Vision and Pattern Recognition, 770

Irshad, 2014, Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential, IEEE Rev. Biomed. Eng., 7, 97, 10.1109/RBME.2013.2295804

Kocjan, 2006, Diagnostic dilemmas in FNAC cytology: difficult breast lesions, 181

Langer, 2015, Computer-aided diagnostics in digital pathology: automated evaluation of early-phase pancreatic cancer in mice, Int. J. Comput. Assist. Radiol. Surg., 10, 1043, 10.1007/s11548-014-1122-9

Liu, 2016, Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process, Med. Image Anal., 32, 281, 10.1016/j.media.2016.04.007

Mathers, 2008

Patel, 1986, Knowledge based solution strategies in medical reasoning, Cognit. Sci., 10, 91, 10.1207/s15516709cog1001_4

Rodenacker, 2003, A feature set for cytometry on digitized microscopic images, Anal. Cell. Pathol., 25, 1, 10.1155/2003/548678

Saha, 2016, Computer-aided diagnosis of breast cancer using cytological images: a systematic review, Tissue Cell, 48, 461, 10.1016/j.tice.2016.07.006

Sharma, 2010, Various types and management of breast cancer: an overview, J. Adv. Pharm. Technol. Res., 1, 109, 10.4103/2231-4040.72251

Simonyan, 2015, Very deep convolutional networks for large-scale image recognition, 3rd IAPR Asian Conference on Pattern Recognition (ACPR), 730

Sirinukunwattana, 2016, Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images, IEEE Trans. Med. Imaging, 35, 1196, 10.1109/TMI.2016.2525803

Spanhol, 2016, Breast cancer histopathological image classification using convolutional neural networks., Proc. Int. Jt. Conf. Neural Net

Spanhol, 2016, A dataset for breast cancer histopathological image classification, IEEE Trans. Biomed. Eng., 63, 1455, 10.1109/TBME.2015.2496264

Szegedy, 2015, Going deeper with convolutions., Proc. IEEE Conf. Comp. Vis. Patt. Recognit., 1

Szegedy, 2016, Rethinking the inception architecture for computer vision, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2818

West, 2000, Model selection for a medical diagnostic decision support system: a breast cancer detection case, Artif. Intell. Med., 20, 183, 10.1016/S0933-3657(00)00063-4

Wolberg, 1990, Multisurface method of pattern separation for medical diagnosis applied to breast cytology, Proc. Natl. Acad. Sci., 87, 9193, 10.1073/pnas.87.23.9193

Xu, 2016, Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images, IEEE Trans. Med. Imaging, 35, 119, 10.1109/TMI.2015.2458702