Addressing architectural distortion in mammogram using AlexNet and support vector machine
Tài liệu tham khảo
World health organization
National institute of cancer prevention and research
Banik, 2011, Rényi entropy of angular spread for detection of architectural distortion in prior mammograms, 609
Petrou, 2006
Anuradha, 2016, Fusion of local and global features for classification of abnormality in mammograms, Sādhanā, 41, 385, 10.1007/s12046-016-0482-y
2017
Amit, 2016, Characterization of architectural distortion in mammograms based on texture analysis using support vector machine classifier with clinical evaluation, J Digit Imag, 29, 104, 10.1007/s10278-015-9807-3
2017
Rangayyan, 2013, Detection of architectural distortion in prior mammograms via analysis of oriented patterns, JoVE, 10.3791/50341
Rangayyan, 2006, Gabor filters and phase portraits for the detection of architectural distortion in mammograms, Med Biol Eng Comput, 44, 883, 10.1007/s11517-006-0088-3
Rangayyan, 2010, Computer-aided detection of architectural distortion in prior mammograms of interval cancer, J Digit Imag, 23, 611, 10.1007/s10278-009-9257-x
Banik, 2010, Detection of architectural distortion in prior mammograms, IEEE Trans Med Imag, 30, 279, 10.1109/TMI.2010.2076828
Olaide Nathaniel Oyelade, 2020, A state-of-the-art survey on deep learning methods for detection of architectural distortion from digital mammography, IEEE Access, 8, 148644, 10.1109/ACCESS.2020.3016223
Liu, 2016, A new feature selection method for the detection of architectural distortion in mammographic images, vol. 10033, 1003341
Liu, 2018, Multiple tbsvm-rfe for the detection of architectural distortion in mammographic images, Multimed Tool Appl, 77, 15773, 10.1007/s11042-017-5150-7
Zyout, 2018, A computer-aided detection of the architectural distortion in digital mammograms using the fractal dimension measurements of bemd, Comput Med Imag Graph, 70, 173, 10.1016/j.compmedimag.2018.04.001
Olawuyi, 2013, Detecting architectural distortion in mammograms using a gabor filtered probability map algorithm, 328
Helder, 2017, A new texture descriptor based on local micro-pattern for detection of architectural distortion in mammographic images, vol. 10134, 101342U
Akhtar, 2018, Detection of architectural distortion from the ridges in a digitized mammogram, Signal, Image Video Processing, 12, 1285, 10.1007/s11760-018-1281-1
Yoshikawa, 2014, Automated detection of architectural distortion using improved adaptive gabor filter, 606
Jasionowska, 2010, A two-step method for detection of architectural distortions in mammograms, 73
Jasionowska, 2019, Wavelet-like selective representations of multidirectional structures: a mammography case, Pattern Anal Appl, 22, 1399, 10.1007/s10044-018-0698-z
Kingsbury, 1999, Image processing with complex wavelets, Philos Trans R Soc London, Ser A: Math. Phys. Eng. Sci., 357, 2543, 10.1098/rsta.1999.0447
Ben-Ari, 2017, Domain specific convolutional neural nets for detection of architectural distortion in mammograms, 552
Ding, 2018, Alexnet feature extraction and multi-kernel learning for object-oriented classification, Int Arch Photogram Rem Sens Spatial Inf Sci, 42, 277, 10.5194/isprs-archives-XLII-3-277-2018
Abdelhafiz, 2019, Deep convolutional neural networks for mammography: advances, challenges and applications, BMC Bioinf, 20, 281, 10.1186/s12859-019-2823-4
Goh, 2020, Architecture distortion score (ads) in malignancy risk stratification of architecture distortion on contrast-enhanced digital mammography, Eur Radiol, 1
2017
Lee, 2017, A curated mammography data set for use in computer-aided detection and diagnosis research, Sci Data, 4, 170177, 10.1038/sdata.2017.177
Clark, 2013, The cancer imaging archive (tcia): maintaining and operating a public information repository, J Digit Imag, 26, 1045, 10.1007/s10278-013-9622-7
Radzi, 2020, Impact of image contrast enhancement on stability of radiomics feature quantification on a 2d mammogram radiograph, IEEE Access, 8, 127720, 10.1109/ACCESS.2020.3008927
Ragab, 2019, Breast cancer detection using deep convolutional neural networks and support vector machines, PeerJ, 7, 10.7717/peerj.6201
Pitaloka, 2017, Enhancing cnn with preprocessing stage in automatic emotion recognition, Procedia Comput Sci, 116, 523, 10.1016/j.procs.2017.10.038
Heidari, 2020, Improving the performance of cnn to predict the likelihood of covid-19 using chest x-ray images with preprocessing algorithms, Int J Med Inf, 144, 104284, 10.1016/j.ijmedinf.2020.104284
Olaide N Oyelade and Absalom E Ezugwu. A deep learning model using data augmentation for detection of architectural distortion in whole and patches of images. Biomed Signal Process Contr, 65:102366..
Costa, 2018
Wang, 2019, Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks, Neurocomputing, 338, 34, 10.1016/j.neucom.2019.01.103
Jasionowska, 2019, Wavelet convolution neural network for classification of spiculated findings in mammograms, 199
Drukker, 2017, Deep learning and three-compartment breast imaging in breast cancer diagnosis, vol. 10134, 101341F
Haralick, 1973, Textural features for image classification, IEEE Trans Syst Man, Cybern, 610, 10.1109/TSMC.1973.4309314