Computer-Aided Detection and Diagnosis of Breast Cancer: a Review
Abstract Statistics across different countries point to breast cancer being among severe cancers with a high mortality rate. Early detection is essential when it comes to reducing the severity and mortality of breast cancer. Researchers proposed many computer-aided diagnosis/detection (CAD) techniques for this purpose. Many perform well (over 90% of classification accuracy, sensitivity, specificity, and f-1 sore), nevertheless, there is still room for improvement. This paper reviews literature related to breast cancer and the challenges faced by the research community. It discusses the common stages of breast cancer detection/ diagnosis using CAD models along with deep learning and transfer learning (TL) methods. In recent studies, deep learning models outperformed the handcrafted feature extraction and classification task and the semantic segmentation of ROI images achieved good results. An accuracy of up to 99.8% has been obtained using these techniques. Furthermore, using TL, researchers combine the power of both, pre-trained deep learning-based networks and traditional feature extraction approaches.
- Referencias
- Cómo citar
- Del mismo autor
- Métricas
Aghdam, H. H., Puig, D., and Solanas, A. (2014). Adaptive Probabilistic Thresholding Method for Accurate Breast Region Segmentation in Mammograms. IEEE International Conference on Pattern Recognition, 3357–3362. https://doi.org/10.1109/ICPR.2014.578
Alam, T., Shia, W. C., Hsu, F. R., and Hassan, T. (2023). Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework. Biomedicines, 11(6), 1536. https://doi.org/10.3390/biomedicines11061536
Alkhaleefah, M., Tan, T. H., Chang, C. H., Wang, T. C., Ma, S. C., Chang, L., and Chang, Y. L. (2022). Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images. Cancers, 14(16), 4030. https://doi.org/10.3390/cancers14164030
American Cancer Society- Cancer facts and figures – ACS. (2013-2022).
Andersson, I., Hildell, J., Muhlow, A., and Pettersson, H. (1978). Number of projections in mammography: influence on detection of breast disease. American Journal of Roentgenology, 130(2), 349–351. https://doi.org/10.2214/ajr.130.2.349
Anitha, J., and Peter, J. D. (2015). Mammogram segmentation using maximal cell strength updation in cellular automata. Medical and Biological Engineering and Computing, 53(8), 737–749. https://doi.org/10.1007/s11517-015-1280-0
Asadi, B., and Memon, Q. (2023). Efficient breast cancer detection via cascade deep learning network. International Journal of Intelligent Networks, 4, 46–52. https://doi.org/10.1016/j.ijin.2023.02.001
Azour, F., and Boukerche, A. (2023). An Efficient Transfer and Ensemble Learning based Computer Aided Breast Abnormality Diagnosis System. IEEE Access, 11, 21199–21209. https://doi.org/10.1109/ACCESS.2022.3192857
Baccouche, A., Garcia-Zapirain, B., Castillo Olea, C., and Elmaghraby, A. S. (2021). Connected-UNets: a deep learning architecture for breast mass segmentation. NPJ Breast Cancer, 7(1), 151. https://doi.org/10.1038/s41523-021-00358-x
Baker, J. A., Rosen, E. L., Lo, J. Y., Gimenez, E. I., Walsh, R., and Soo, M. S. (2003). Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion. American Journal of Roentgenology, 181(4), 1083–1088. https://doi.org/10.2214/ajr.181.4.1811083
Bobeda, J., García-Gonzalez, M. J., Pérez-Herrera, L. V., and Lopez-Linares, K. (2023, May). Unsupervised Data Drift Detection Using Convolutional Autoencoders: A Breast Cancer Imaging Scenario. International KES Conference on Innovation in Medicine and Healthcare, 345–354. Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-3311-2_31
Breiman, L. (1996). Bagging Predictors. Machine Learning, 26, 123–140. https://doi.org/10.1007/BF00058655
Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324
Byung W. H., and Bong S. S. (2010). Segmentation of Regions of Interest in Mam-mograms in a Topographic Approach. IEEE Transactions on Information Technology in Biomedicine, 14(1), 129–139. https://doi.org/10.1109/TITB.2009.2033269
Chakravarthy, S. S., Bharanidharan, N., and Rajaguru, H. (2023). Processing of digital mammogram images using optimized ELM with deep transfer learning for breast cancer diagnosis. Multimedia Tools and Applications, 82(30), 47585–47609. https://doi.org/10.1007/s11042-023-15265-5
Chen, Z., Yang, J., Li, S., Lv, M., Shen, Y., Wang, B., … and Yang, J. (2017). Inva-sive lobular carcinoma of the breast: a special histological type compared with invasive ductal carcinoma. PLoS One, 12(9), e0182397. https://doi.org/10.1371/journal.pone.0182397
Cheng, Y., Gao, Y., Xie, L., Xie, X., and Lin, W. (2020). Spatial enhanced rotation aware network for breast mass segmentation in digital mammogram. IEEE Access, 10, 92559–92570. https://doi.org/10.1109/access.2020.2978009
Cho, P., and Yoon, H. J. (2021). Evaluation of U-net-based image segmentation model to digital mammography. Medical Imaging 2021: Image Processing, 11596, 593–599. SPIE. https://doi.org/10.1117/12.2581401
Christianini, N., and Taylor, J. C. S. (2000). An Introduction to Support Vector Ma-chines and Other Kernel-Based Learning Methods. Cambridge University Press. https://doi.org/10.1017/CBO9780511801389
Dhamodharan, S., Pichai, S. (2021). Background Preserved and Feature-Oriented Contrast Improvement Using Weighted Cumulative Distribution Function for Digital Mammograms. In: Balasubramaniam, P., Ratnavelu, K., Rajchakit, G., Nagamani, G. Mathematical Modelling and Computational Intelligence Techniques. ICMMCIT 2021. Springer Proceedings in Mathematics and Statistics, 376. Springer, Singapore. https://doi.org/10.1007/978-981-16-6018-4_12
Dibden, A., Offman, J., Duffy, S. W., and Gabe, R. (2020). Worldwide Review and Meta-Analysis of Cohort Studies Measuring the Effect of Mammography Screening Pro-grammes on Incidence-Based Breast Cancer Mortality. Cancers, 12(4), 976. https://doi.org/10.3390/cancers12040976
Freund, Y. and Schapire, R. E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55, 119–139. https://doi.org/10.1006/jcss.1997.1504
Friedman, J. H., J. Bentely, and Finkel, R. A. (1977). An Algorithm for Finding Best Matches in Logarithmic Expected Time. ACM Transactions on Mathematical Software 3(3), 209–226. https://doi.org/10.1145/355744.355745
Ganesh, K., and Rao, B. P. (2023). Classification of Breast Cancer from Mammogram Images using DenseNET. Journal of Biomedical Engineering, 40(2), 192–199.
Ghiasi G. (2021). Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2917–2927. https://doi.org/10.1109/CVPR46437.2021.00294
Guo, Y., Hastie, T., and Tibshirani, R. (2007). Regularized linear discriminant analysis and its application in microarrays. Biostatistics, 8(1), 86–100. https://doi.org/10.1093/biostatistics/kxj035
Gupta, B., Tiwari, M. (2017). A tool supported approach for brightness preserving contrast enhancement and mass segmentation of mammogram images using histogram modified grey relational analysis. Multidim Syst Sign Process, 28, 1549–1567. https://doi.org/10.1007/s11045-016-0432-1
Hamad, Y. A., Seno, M. E., Safonova, A. N., & Shakir, S. (2022). Breast Tumor Segmentation on Medical Images using Combination of Fuzzy Clustering and Threshold. Computer Integrated Manufacturing Systems, 28(10), 70–83.
Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction 2, 1–758. New York: springer. https://doi.org/10.1007/b94608_8
Heath, M., Bowyer K., Kopans D., Kegelmeyer P., Moore R., and Chang K. (1998). Current status of the digital database for screening mammography. Digital Mammography. Computational Imaging and Vision, 13, 457–460. https://doi.org/10.1007/978-94-011-5318-8_75
Kaiming, H., Xiangyu, Z., Shaoqing, R., and Jian, S. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. IEEE international conference on computer vision, 1026–1034. https://doi.org/10.1109/ICCV.2015.123
King, M. A., Doherty, P. W., Schwinger, R. B., and Penney, B. C. (1983). A Wiener filter for nuclear medicine images. Medical physics, 10(6), 876–880. https://doi.org/10.1118/1.595352
Kulkarni, S., and Rabidas, R. (2023). Fully convolutional network for automated detection and diagnosis of mammographic masses. Multimedia Tools and Applications, 82, 44819–44840 https://doi.org/10.1007/s11042-023-14757-8
Kumar, I., Kumar, A., Kumar, V. A., Kannan, R., Vimal, V., Singh, K. U., and Mahmud, M. (2022). Dense tissue pattern characterization using deep neural network. Cognitive computation, 14(5), 1728–1751. https://doi.org/10.1007/s12559-021-09970-2
Li, B., Friedman, J., Olshen, R., and Stone, C. (1984). Classification and regression trees (CART). Biometrics, 40(3), 358–361. https://doi.org/10.2307/2530946
Li, H., Chen, D., Nailon, W. H., Davies, M. E., and Laurenson, D. I. (2021). Dual convolutional neural networks for breast mass segmentation and diagnosis in mammography. IEEE Transactions on Medical Imaging, 41(1), 3–13. https://doi.org/10.1109/TMI.2021.3102622
Li, H., Zhuang, S., Li, D., Zhao, J., and Ma, Y. (2019). Benign and malignant classification of mammogram images based on deep learning. Biomedical Signal Processing and Control, 51, 347–354. https://doi.org/10.1016/j.bspc.2019.02.017
Loizidou, K., Skouroumouni, G., Nikolaou, C., and Pitris, C. (2020). An automated breast micro-calcification detection and classification technique using temporal subtraction of mammograms. IEEE Access, 8, 52785–52795. https://doi.org/10.1109/ACCESS.2020.2980616
Lopez, M. G., Posada, N., Moura, D. C., Pollan, R. R., Valiente, J. M. F., Ortega, C. S., and Araújo, B. M. F. (2012). BCDR: a breast cancer digital repository. 15th International conference on experimental mechanics, 1215, 1–5. Porto, Portugal.
Mabrouk, M. S., Afify, H. M., and Marzouk, S. Y. (2019). Fully automated computer-aided diagnosis system for micro calcifications cancer based on improved mammographic image techniques. Ain Shams Engineering Journal, 10(3), 517–527. https://doi.org/10.1016/j.asej.2019.01.009
Michael, H., Kevin, B., Daniel, K., Richard, M., and Philip, W. K. (2001). The Digital Database for Screening Mammography. In M. J. Yaffe (ed.), International Workshop on Digital Mammography, 212–218. Medical Physics Publishing.
Misra, S., Solomon, N. L., Moffat, F. L., and Koniaris, L. G. (2010). Screening criteria for breast cancer. Advances In Surgery, 44, 87–100. https://doi.org/10.1016/j.yasu.2010.05.008
Mohanty, F., Rup, S., Dash, B., Majhi, B., and Swamy, M. N. S. (2020). An improved scheme for digital mammogram classification using weighted chaotic salp swarm algorithm-based kernel extreme learning machine. Applied Soft Computing, 91, 106266. https://doi.org/10.1016/j.asoc.2020.106266
Moreira, I. C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M. J., and Cardoso, J. S. (2012). Inbreast: toward a full-field digital mammographic database. Academic radiology, 19(2), 236–248. https://doi.org/10.1016/j.acra.2011.09.014
Nalawade, Y. V. (2009). Evaluation of breast calcifications. The Indian Journal of Radiology and Imaging, 19(4), 282–286. https://doi.org/10.4103/0971-3026.57208
Oza, P., Sharma, P., and Patel, S. (2023). Deep ensemble transfer learning-based framework for mammographic image classification. The Journal of Supercomputing, 79(7), 8048–8069. https://doi.org/10.1007/s11227-022-04992-5
Patel, J. J., and Hadia, S. K. (2023). Two-Stage Feature Selection Method Created for 20 Neurons Artificial Neural Networks for Automatic Breast Cancer Detection. Trends in Sciences, 20(2), 4027–4027. https://doi.org/10.48048/tis.2023.4027
Pati, A., Parhi, M., Pattanayak, B. K., Singh, D., Singh, V., Kadry, S., and Kang, B. G. (2023). Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing. Diagnostics, 13(13), 2191. https://doi.org/10.3390/diagnostics13132191
Petrini, D. G., Shimizu, C., Roela, R. A., Valente, G. V., Folgueira, M. A. A. K., and Kim, H. Y. (2022). Breast Cancer Diagnosis in Two-View Mammography. Using End-to-End Trained EfficientNet-Based Convolutional Network. IEEE Access, 10, 77723–77731. https://doi.org/10.1109/ACCESS.2022.3193250
Rahmati, P., Hamarneh, G., Nussbaum, D., Adler, A. (2010). A New Preprocessing Filter for Digital Mammograms. In A. Elmoataz, O. Lezoray, F. Nouboud, D. Mammass, and J. Meunier (eds.), Image and Signal Processing. ICISP 2010. Lecture Notes in Computer Science, vol. 6134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13681-8_68
Raj, R., Mathew, J., Kannath, S. K., and Rajan, J. (2022). Crossover based technique for data augmentation. Computer Methods and Programs in Biomedicine, 218, 106716. https://doi.org/10.1016/j.cmpb.2022.106716
Rajalakshmi, N. R., Vidhyapriya, R., Elango, N. and Ramesh, N. (2020). Deeply supervised U-Net for mass segmentation in digital mammograms. International Journal of Imaging Systems and Technology, 31(1), 59–71. https://doi.org/10.1002/ima.22516
Ramani, R., Vanitha, N. S., and Valarmathy, S. (2013). The preprocessing techniques for breast cancer detection in mammography images. International Journal of Image, Graphics and Signal Processing, 5(5), 47. https://doi.org/10.5815/ijigsp.2013.05.06
Ranjbarzadeh, R., Nazanin, T. S., Saeid, J. G., Mohammad, S. E., Mahboub, P., Yaghoub, P., Shokofeh, A., and Malika, B., (2022). MRFE-CNN: Multi-route feature extraction model for breast tumor segmentation in Mammograms using a convolutional neural network. Annals of Operations Research, 1–22. https://doi.org/10.1007/s10479-022-04755-8
Ravikumar, M., Rachana, P. G., and Shivaprasad, B. J. (2023). Segmentation of tumour from mammogram images using U-SegNet: a hybrid approach. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 11(3), 387–398. https://doi.org/10.1080/21681163.2022.2072769
Rebecca, S. L., Francisco, G., Assaf, H., and Daniel, R. (2016). Curated Breast Imaging Subset of DDSM. The Cancer Imaging Archive.
Rmili, M., Moutaouakkil, A. E., and Saleck, M. M. (2022). Hybrid Mammogram Segmentation Using Watershed and Region Growing. In Advances in Information, Communication and Cybersecurity: Proceedings of ICI2C'21 (pp. 23–32). Springer International Publishing. https://doi.org/10.1007/978-3-030-91738-8_3
Schapire, R. E., Freund, Y., Bartlett, P. L., and Lee, W. S. (1998). Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics, 26(5), 1651–1686. https://doi.org/10.1214/aos/1024691352
Seber, G. A. (2009). Multivariate observations. John Wiley and Sons.
Shahrokhy, S. M. (2004). Visual and statistical quality assessment and improvement of remotely sensed images. ISPRS Proceedings XXXV (950).
Shamim, H. M. (2022). Micro Calcification Segmentation Using Modified U-net Segmentation Network from Mammogram. Journal of King Saud University - Computer and Information Sciences, 34(2), pp. 86–94. https://doi.org/10.1016/j.jksuci.2019.10.014
Sharma, B. P., and Purwar, R. K. (2020). Dual thresholding based Breast cancer detection in Mammograms. IEEE World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), 589–592). https://doi.org/10.1109/WorldS450073.2020.9210323
Sharma, B. P., and Purwar, R. K. (2022). Ensemble Boosted Tree based Mammogram image classification using Texture features and extracted smart features of Deep Neural Network. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(4), 419–434. https://doi.org/10.14201/ADCAIJ2021104419434
Sharma, B. P., and Purwar, R. K. (2023). An augmented mammogram image dataset and its performance analysis for various classification models. Multimedia Tools and Applications, 82, 32011–32055. https://doi.org/10.1007/s11042-023-14566-z
Sharma, M. K., Jas, M., Karale, V., Sadhu, A., and Mukhodhyay, S. (2019). Mammogram segmentation using multi-atlas deformable registration. Computers in Biology and Medicine, 110, 244–253. https://doi.org/10.1016/j.compbiomed.2019.06.001
Sickles, E. A., Weber, W. N., Galvin, H. B., Ominsky, S. H., and Sollitto, R. A. (1986). Baseline screening mammography: one vs two views per breast. American Journal of Roentgenology, 147(6), 1149–1153. https://doi.org/10.2214/ajr.147.6.1149
Suckling, J. (1994). The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series 1069, 375–378).
Taghanaki, S. A., Liu, Y., Miles B. and Hamarneh, G. (2017). Geometry-Based Pectoral Muscle Segmentation From MLO Mammogram Views. IEEE Transactions on Biomedical Engineering, 64(11), 2662–2671. https://doi.org/10.1109/TBME.2017.2649481
Talmi, I., Mechrez, R., and Zelnik-Manor, L. (2017). Template matching with deformable diversity similarity. IEEE Conference on Computer Vision and Pattern Recognition, 175–183. https://doi.org/10.1109/CVPR.2017.144
Taylor, L., Nitschke, G. (2018). Improving deep learning with generic data augmentation. IEEE Symposium Series on Computational Intelligence (SSCI), 1542–1547. https://doi.org/10.1109/SSCI.2018.8628742
Tiryaki, V. M. (2023). Mass segmentation and classification from film mammograms using cascaded deep transfer learning. Biomedical Signal Processing and Control, 84, 104819. https://doi.org/10.1016/j.bspc.2023.104819
Uthoff, J., and Sieren, J. C. (2018). Information theory optimization based feature selection in breast mammography lesion classification. IEEE International Symposium on Biomedical Imaging (ISBI 2018), 817–821. https://doi.org/10.1109/ISBI.2018.8363697
Vidal, J., Vilanova, J. C., and Martí, R. (2022). A U-Net Ensemble for breast lesion segmentation in DCE MRI. Computers in Biology and Medicine, 140, 105093. https://doi.org/10.1016/j.compbiomed.2021.105093
Welch, H. G., Prorok, P. C., OMalley, A. J., and Kramer, B. S. (2016). Breast-cancer tumor size, overdiagnosis, and mammography screening effectiveness. New England Journal of Medicine, 375(15), 1438–1447. https://doi.org/10.1056/NEJMoa1600249
Yang, H., and Zhou, Y. (2021). IDA-GAN: A Novel Imbalanced Data Augmentation GAN. International Conference on Pattern Recognition (ICPR). https://doi.org/10.1109/ICPR48806.2021.9411996
Zebari, D. A., Ibrahim, D. A., Zeebaree, D. Q., Mohammed, M. A., Haron, H., Zebari, N. A., and Maskeliunas, R. (2021). Breast cancer detection using mammogram images with improved multi-fractal dimension approach and feature fusion. Applied Sciences, 11(24), 12122. https://doi.org/10.3390/app112412122
Zemmal, N., Azizi, N., Ziani, A., Benzebouchi, N. E., and Aldwairi, M. (2019). An enhanced feature selection approach based on mutual information for breast cancer diagnosis. 2019 6th international conference on image and signal processing and their applications (ISPA), 1–6. IEEE. https://doi.org/10.1109/ISPA48434.2019.8966803
Zhang, H., Wu, R., Yuan, T., Jiang, Z., Huang, S., Wu, J., … and Ji, D. (2020). DE-Ada*: A novel model for breast mass classification using cross-modal pathological semantic mining and organic integration of multi-feature fusions. Information Sciences, 539, 461–486. https://doi.org/10.1016/j.ins.2020.05.080
Zhong, Z., Zheng, L., Kang, G., Li, S., and Yang, Y. (2020). Random Erasing Data Augmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 13001–13008. https://doi.org/10.1609/aaai.v34i07.7000
Zuiderveld, K. (1994). Contrast Limited Adaptive Histograph Equalization. Graphic Gems IV, 474–485. San Diego: Academic Press Professional. https://doi.org/10.1016/B978-0-12-336156-1.50061-6
Alam, T., Shia, W. C., Hsu, F. R., and Hassan, T. (2023). Improving Breast Cancer Detection and Diagnosis through Semantic Segmentation Using the Unet3+ Deep Learning Framework. Biomedicines, 11(6), 1536. https://doi.org/10.3390/biomedicines11061536
Alkhaleefah, M., Tan, T. H., Chang, C. H., Wang, T. C., Ma, S. C., Chang, L., and Chang, Y. L. (2022). Connected-SegNets: A Deep Learning Model for Breast Tumor Segmentation from X-ray Images. Cancers, 14(16), 4030. https://doi.org/10.3390/cancers14164030
American Cancer Society- Cancer facts and figures – ACS. (2013-2022).
Andersson, I., Hildell, J., Muhlow, A., and Pettersson, H. (1978). Number of projections in mammography: influence on detection of breast disease. American Journal of Roentgenology, 130(2), 349–351. https://doi.org/10.2214/ajr.130.2.349
Anitha, J., and Peter, J. D. (2015). Mammogram segmentation using maximal cell strength updation in cellular automata. Medical and Biological Engineering and Computing, 53(8), 737–749. https://doi.org/10.1007/s11517-015-1280-0
Asadi, B., and Memon, Q. (2023). Efficient breast cancer detection via cascade deep learning network. International Journal of Intelligent Networks, 4, 46–52. https://doi.org/10.1016/j.ijin.2023.02.001
Azour, F., and Boukerche, A. (2023). An Efficient Transfer and Ensemble Learning based Computer Aided Breast Abnormality Diagnosis System. IEEE Access, 11, 21199–21209. https://doi.org/10.1109/ACCESS.2022.3192857
Baccouche, A., Garcia-Zapirain, B., Castillo Olea, C., and Elmaghraby, A. S. (2021). Connected-UNets: a deep learning architecture for breast mass segmentation. NPJ Breast Cancer, 7(1), 151. https://doi.org/10.1038/s41523-021-00358-x
Baker, J. A., Rosen, E. L., Lo, J. Y., Gimenez, E. I., Walsh, R., and Soo, M. S. (2003). Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion. American Journal of Roentgenology, 181(4), 1083–1088. https://doi.org/10.2214/ajr.181.4.1811083
Bobeda, J., García-Gonzalez, M. J., Pérez-Herrera, L. V., and Lopez-Linares, K. (2023, May). Unsupervised Data Drift Detection Using Convolutional Autoencoders: A Breast Cancer Imaging Scenario. International KES Conference on Innovation in Medicine and Healthcare, 345–354. Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-99-3311-2_31
Breiman, L. (1996). Bagging Predictors. Machine Learning, 26, 123–140. https://doi.org/10.1007/BF00058655
Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324
Byung W. H., and Bong S. S. (2010). Segmentation of Regions of Interest in Mam-mograms in a Topographic Approach. IEEE Transactions on Information Technology in Biomedicine, 14(1), 129–139. https://doi.org/10.1109/TITB.2009.2033269
Chakravarthy, S. S., Bharanidharan, N., and Rajaguru, H. (2023). Processing of digital mammogram images using optimized ELM with deep transfer learning for breast cancer diagnosis. Multimedia Tools and Applications, 82(30), 47585–47609. https://doi.org/10.1007/s11042-023-15265-5
Chen, Z., Yang, J., Li, S., Lv, M., Shen, Y., Wang, B., … and Yang, J. (2017). Inva-sive lobular carcinoma of the breast: a special histological type compared with invasive ductal carcinoma. PLoS One, 12(9), e0182397. https://doi.org/10.1371/journal.pone.0182397
Cheng, Y., Gao, Y., Xie, L., Xie, X., and Lin, W. (2020). Spatial enhanced rotation aware network for breast mass segmentation in digital mammogram. IEEE Access, 10, 92559–92570. https://doi.org/10.1109/access.2020.2978009
Cho, P., and Yoon, H. J. (2021). Evaluation of U-net-based image segmentation model to digital mammography. Medical Imaging 2021: Image Processing, 11596, 593–599. SPIE. https://doi.org/10.1117/12.2581401
Christianini, N., and Taylor, J. C. S. (2000). An Introduction to Support Vector Ma-chines and Other Kernel-Based Learning Methods. Cambridge University Press. https://doi.org/10.1017/CBO9780511801389
Dhamodharan, S., Pichai, S. (2021). Background Preserved and Feature-Oriented Contrast Improvement Using Weighted Cumulative Distribution Function for Digital Mammograms. In: Balasubramaniam, P., Ratnavelu, K., Rajchakit, G., Nagamani, G. Mathematical Modelling and Computational Intelligence Techniques. ICMMCIT 2021. Springer Proceedings in Mathematics and Statistics, 376. Springer, Singapore. https://doi.org/10.1007/978-981-16-6018-4_12
Dibden, A., Offman, J., Duffy, S. W., and Gabe, R. (2020). Worldwide Review and Meta-Analysis of Cohort Studies Measuring the Effect of Mammography Screening Pro-grammes on Incidence-Based Breast Cancer Mortality. Cancers, 12(4), 976. https://doi.org/10.3390/cancers12040976
Freund, Y. and Schapire, R. E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55, 119–139. https://doi.org/10.1006/jcss.1997.1504
Friedman, J. H., J. Bentely, and Finkel, R. A. (1977). An Algorithm for Finding Best Matches in Logarithmic Expected Time. ACM Transactions on Mathematical Software 3(3), 209–226. https://doi.org/10.1145/355744.355745
Ganesh, K., and Rao, B. P. (2023). Classification of Breast Cancer from Mammogram Images using DenseNET. Journal of Biomedical Engineering, 40(2), 192–199.
Ghiasi G. (2021). Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2917–2927. https://doi.org/10.1109/CVPR46437.2021.00294
Guo, Y., Hastie, T., and Tibshirani, R. (2007). Regularized linear discriminant analysis and its application in microarrays. Biostatistics, 8(1), 86–100. https://doi.org/10.1093/biostatistics/kxj035
Gupta, B., Tiwari, M. (2017). A tool supported approach for brightness preserving contrast enhancement and mass segmentation of mammogram images using histogram modified grey relational analysis. Multidim Syst Sign Process, 28, 1549–1567. https://doi.org/10.1007/s11045-016-0432-1
Hamad, Y. A., Seno, M. E., Safonova, A. N., & Shakir, S. (2022). Breast Tumor Segmentation on Medical Images using Combination of Fuzzy Clustering and Threshold. Computer Integrated Manufacturing Systems, 28(10), 70–83.
Hastie, T., Tibshirani, R., Friedman, J. H., and Friedman, J. H. (2009). The elements of statistical learning: data mining, inference, and prediction 2, 1–758. New York: springer. https://doi.org/10.1007/b94608_8
Heath, M., Bowyer K., Kopans D., Kegelmeyer P., Moore R., and Chang K. (1998). Current status of the digital database for screening mammography. Digital Mammography. Computational Imaging and Vision, 13, 457–460. https://doi.org/10.1007/978-94-011-5318-8_75
Kaiming, H., Xiangyu, Z., Shaoqing, R., and Jian, S. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. IEEE international conference on computer vision, 1026–1034. https://doi.org/10.1109/ICCV.2015.123
King, M. A., Doherty, P. W., Schwinger, R. B., and Penney, B. C. (1983). A Wiener filter for nuclear medicine images. Medical physics, 10(6), 876–880. https://doi.org/10.1118/1.595352
Kulkarni, S., and Rabidas, R. (2023). Fully convolutional network for automated detection and diagnosis of mammographic masses. Multimedia Tools and Applications, 82, 44819–44840 https://doi.org/10.1007/s11042-023-14757-8
Kumar, I., Kumar, A., Kumar, V. A., Kannan, R., Vimal, V., Singh, K. U., and Mahmud, M. (2022). Dense tissue pattern characterization using deep neural network. Cognitive computation, 14(5), 1728–1751. https://doi.org/10.1007/s12559-021-09970-2
Li, B., Friedman, J., Olshen, R., and Stone, C. (1984). Classification and regression trees (CART). Biometrics, 40(3), 358–361. https://doi.org/10.2307/2530946
Li, H., Chen, D., Nailon, W. H., Davies, M. E., and Laurenson, D. I. (2021). Dual convolutional neural networks for breast mass segmentation and diagnosis in mammography. IEEE Transactions on Medical Imaging, 41(1), 3–13. https://doi.org/10.1109/TMI.2021.3102622
Li, H., Zhuang, S., Li, D., Zhao, J., and Ma, Y. (2019). Benign and malignant classification of mammogram images based on deep learning. Biomedical Signal Processing and Control, 51, 347–354. https://doi.org/10.1016/j.bspc.2019.02.017
Loizidou, K., Skouroumouni, G., Nikolaou, C., and Pitris, C. (2020). An automated breast micro-calcification detection and classification technique using temporal subtraction of mammograms. IEEE Access, 8, 52785–52795. https://doi.org/10.1109/ACCESS.2020.2980616
Lopez, M. G., Posada, N., Moura, D. C., Pollan, R. R., Valiente, J. M. F., Ortega, C. S., and Araújo, B. M. F. (2012). BCDR: a breast cancer digital repository. 15th International conference on experimental mechanics, 1215, 1–5. Porto, Portugal.
Mabrouk, M. S., Afify, H. M., and Marzouk, S. Y. (2019). Fully automated computer-aided diagnosis system for micro calcifications cancer based on improved mammographic image techniques. Ain Shams Engineering Journal, 10(3), 517–527. https://doi.org/10.1016/j.asej.2019.01.009
Michael, H., Kevin, B., Daniel, K., Richard, M., and Philip, W. K. (2001). The Digital Database for Screening Mammography. In M. J. Yaffe (ed.), International Workshop on Digital Mammography, 212–218. Medical Physics Publishing.
Misra, S., Solomon, N. L., Moffat, F. L., and Koniaris, L. G. (2010). Screening criteria for breast cancer. Advances In Surgery, 44, 87–100. https://doi.org/10.1016/j.yasu.2010.05.008
Mohanty, F., Rup, S., Dash, B., Majhi, B., and Swamy, M. N. S. (2020). An improved scheme for digital mammogram classification using weighted chaotic salp swarm algorithm-based kernel extreme learning machine. Applied Soft Computing, 91, 106266. https://doi.org/10.1016/j.asoc.2020.106266
Moreira, I. C., Amaral, I., Domingues, I., Cardoso, A., Cardoso, M. J., and Cardoso, J. S. (2012). Inbreast: toward a full-field digital mammographic database. Academic radiology, 19(2), 236–248. https://doi.org/10.1016/j.acra.2011.09.014
Nalawade, Y. V. (2009). Evaluation of breast calcifications. The Indian Journal of Radiology and Imaging, 19(4), 282–286. https://doi.org/10.4103/0971-3026.57208
Oza, P., Sharma, P., and Patel, S. (2023). Deep ensemble transfer learning-based framework for mammographic image classification. The Journal of Supercomputing, 79(7), 8048–8069. https://doi.org/10.1007/s11227-022-04992-5
Patel, J. J., and Hadia, S. K. (2023). Two-Stage Feature Selection Method Created for 20 Neurons Artificial Neural Networks for Automatic Breast Cancer Detection. Trends in Sciences, 20(2), 4027–4027. https://doi.org/10.48048/tis.2023.4027
Pati, A., Parhi, M., Pattanayak, B. K., Singh, D., Singh, V., Kadry, S., and Kang, B. G. (2023). Breast Cancer Diagnosis Based on IoT and Deep Transfer Learning Enabled by Fog Computing. Diagnostics, 13(13), 2191. https://doi.org/10.3390/diagnostics13132191
Petrini, D. G., Shimizu, C., Roela, R. A., Valente, G. V., Folgueira, M. A. A. K., and Kim, H. Y. (2022). Breast Cancer Diagnosis in Two-View Mammography. Using End-to-End Trained EfficientNet-Based Convolutional Network. IEEE Access, 10, 77723–77731. https://doi.org/10.1109/ACCESS.2022.3193250
Rahmati, P., Hamarneh, G., Nussbaum, D., Adler, A. (2010). A New Preprocessing Filter for Digital Mammograms. In A. Elmoataz, O. Lezoray, F. Nouboud, D. Mammass, and J. Meunier (eds.), Image and Signal Processing. ICISP 2010. Lecture Notes in Computer Science, vol. 6134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13681-8_68
Raj, R., Mathew, J., Kannath, S. K., and Rajan, J. (2022). Crossover based technique for data augmentation. Computer Methods and Programs in Biomedicine, 218, 106716. https://doi.org/10.1016/j.cmpb.2022.106716
Rajalakshmi, N. R., Vidhyapriya, R., Elango, N. and Ramesh, N. (2020). Deeply supervised U-Net for mass segmentation in digital mammograms. International Journal of Imaging Systems and Technology, 31(1), 59–71. https://doi.org/10.1002/ima.22516
Ramani, R., Vanitha, N. S., and Valarmathy, S. (2013). The preprocessing techniques for breast cancer detection in mammography images. International Journal of Image, Graphics and Signal Processing, 5(5), 47. https://doi.org/10.5815/ijigsp.2013.05.06
Ranjbarzadeh, R., Nazanin, T. S., Saeid, J. G., Mohammad, S. E., Mahboub, P., Yaghoub, P., Shokofeh, A., and Malika, B., (2022). MRFE-CNN: Multi-route feature extraction model for breast tumor segmentation in Mammograms using a convolutional neural network. Annals of Operations Research, 1–22. https://doi.org/10.1007/s10479-022-04755-8
Ravikumar, M., Rachana, P. G., and Shivaprasad, B. J. (2023). Segmentation of tumour from mammogram images using U-SegNet: a hybrid approach. Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, 11(3), 387–398. https://doi.org/10.1080/21681163.2022.2072769
Rebecca, S. L., Francisco, G., Assaf, H., and Daniel, R. (2016). Curated Breast Imaging Subset of DDSM. The Cancer Imaging Archive.
Rmili, M., Moutaouakkil, A. E., and Saleck, M. M. (2022). Hybrid Mammogram Segmentation Using Watershed and Region Growing. In Advances in Information, Communication and Cybersecurity: Proceedings of ICI2C'21 (pp. 23–32). Springer International Publishing. https://doi.org/10.1007/978-3-030-91738-8_3
Schapire, R. E., Freund, Y., Bartlett, P. L., and Lee, W. S. (1998). Boosting the margin: A new explanation for the effectiveness of voting methods. Annals of Statistics, 26(5), 1651–1686. https://doi.org/10.1214/aos/1024691352
Seber, G. A. (2009). Multivariate observations. John Wiley and Sons.
Shahrokhy, S. M. (2004). Visual and statistical quality assessment and improvement of remotely sensed images. ISPRS Proceedings XXXV (950).
Shamim, H. M. (2022). Micro Calcification Segmentation Using Modified U-net Segmentation Network from Mammogram. Journal of King Saud University - Computer and Information Sciences, 34(2), pp. 86–94. https://doi.org/10.1016/j.jksuci.2019.10.014
Sharma, B. P., and Purwar, R. K. (2020). Dual thresholding based Breast cancer detection in Mammograms. IEEE World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4), 589–592). https://doi.org/10.1109/WorldS450073.2020.9210323
Sharma, B. P., and Purwar, R. K. (2022). Ensemble Boosted Tree based Mammogram image classification using Texture features and extracted smart features of Deep Neural Network. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 10(4), 419–434. https://doi.org/10.14201/ADCAIJ2021104419434
Sharma, B. P., and Purwar, R. K. (2023). An augmented mammogram image dataset and its performance analysis for various classification models. Multimedia Tools and Applications, 82, 32011–32055. https://doi.org/10.1007/s11042-023-14566-z
Sharma, M. K., Jas, M., Karale, V., Sadhu, A., and Mukhodhyay, S. (2019). Mammogram segmentation using multi-atlas deformable registration. Computers in Biology and Medicine, 110, 244–253. https://doi.org/10.1016/j.compbiomed.2019.06.001
Sickles, E. A., Weber, W. N., Galvin, H. B., Ominsky, S. H., and Sollitto, R. A. (1986). Baseline screening mammography: one vs two views per breast. American Journal of Roentgenology, 147(6), 1149–1153. https://doi.org/10.2214/ajr.147.6.1149
Suckling, J. (1994). The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series 1069, 375–378).
Taghanaki, S. A., Liu, Y., Miles B. and Hamarneh, G. (2017). Geometry-Based Pectoral Muscle Segmentation From MLO Mammogram Views. IEEE Transactions on Biomedical Engineering, 64(11), 2662–2671. https://doi.org/10.1109/TBME.2017.2649481
Talmi, I., Mechrez, R., and Zelnik-Manor, L. (2017). Template matching with deformable diversity similarity. IEEE Conference on Computer Vision and Pattern Recognition, 175–183. https://doi.org/10.1109/CVPR.2017.144
Taylor, L., Nitschke, G. (2018). Improving deep learning with generic data augmentation. IEEE Symposium Series on Computational Intelligence (SSCI), 1542–1547. https://doi.org/10.1109/SSCI.2018.8628742
Tiryaki, V. M. (2023). Mass segmentation and classification from film mammograms using cascaded deep transfer learning. Biomedical Signal Processing and Control, 84, 104819. https://doi.org/10.1016/j.bspc.2023.104819
Uthoff, J., and Sieren, J. C. (2018). Information theory optimization based feature selection in breast mammography lesion classification. IEEE International Symposium on Biomedical Imaging (ISBI 2018), 817–821. https://doi.org/10.1109/ISBI.2018.8363697
Vidal, J., Vilanova, J. C., and Martí, R. (2022). A U-Net Ensemble for breast lesion segmentation in DCE MRI. Computers in Biology and Medicine, 140, 105093. https://doi.org/10.1016/j.compbiomed.2021.105093
Welch, H. G., Prorok, P. C., OMalley, A. J., and Kramer, B. S. (2016). Breast-cancer tumor size, overdiagnosis, and mammography screening effectiveness. New England Journal of Medicine, 375(15), 1438–1447. https://doi.org/10.1056/NEJMoa1600249
Yang, H., and Zhou, Y. (2021). IDA-GAN: A Novel Imbalanced Data Augmentation GAN. International Conference on Pattern Recognition (ICPR). https://doi.org/10.1109/ICPR48806.2021.9411996
Zebari, D. A., Ibrahim, D. A., Zeebaree, D. Q., Mohammed, M. A., Haron, H., Zebari, N. A., and Maskeliunas, R. (2021). Breast cancer detection using mammogram images with improved multi-fractal dimension approach and feature fusion. Applied Sciences, 11(24), 12122. https://doi.org/10.3390/app112412122
Zemmal, N., Azizi, N., Ziani, A., Benzebouchi, N. E., and Aldwairi, M. (2019). An enhanced feature selection approach based on mutual information for breast cancer diagnosis. 2019 6th international conference on image and signal processing and their applications (ISPA), 1–6. IEEE. https://doi.org/10.1109/ISPA48434.2019.8966803
Zhang, H., Wu, R., Yuan, T., Jiang, Z., Huang, S., Wu, J., … and Ji, D. (2020). DE-Ada*: A novel model for breast mass classification using cross-modal pathological semantic mining and organic integration of multi-feature fusions. Information Sciences, 539, 461–486. https://doi.org/10.1016/j.ins.2020.05.080
Zhong, Z., Zheng, L., Kang, G., Li, S., and Yang, Y. (2020). Random Erasing Data Augmentation. Proceedings of the AAAI Conference on Artificial Intelligence, 34(07), 13001–13008. https://doi.org/10.1609/aaai.v34i07.7000
Zuiderveld, K. (1994). Contrast Limited Adaptive Histograph Equalization. Graphic Gems IV, 474–485. San Diego: Academic Press Professional. https://doi.org/10.1016/B978-0-12-336156-1.50061-6
Sharma, B. P., & Purwar, R. K. (2024). Computer-Aided Detection and Diagnosis of Breast Cancer: a Review. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 13(1), e31412. https://doi.org/10.14201/adcaij.31412
Most read articles by the same author(s)
- Bhanu Prakash Sharma, Ravindra Kumar Purwar, Ensemble Boosted Tree based Mammogram image classification using Texture features and extracted smart features of Deep Neural Network , ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal: Vol. 10 No. 4 (2021)
Downloads
Download data is not yet available.
+
−