A Review on Breast Cancer Prediction Using Machine Learning and Deep Learning Techniques
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Abstract
Breast cancer is one of the most prevalent and chronic disease that affect women. To overcome this disease, effective medical treatment is required. Early detection of the disease plays an important role for suitable medication and survival of patient. To identify the breast cancer in the patients, standard imaging technique mammography is used. Due to the subtle and varied nature of cancer tissues interpreting mammogram images can be a challenge to doctors. Machine learning (ML) and Deep Learning (DL) techniques offer promising solutions that provide efficient breast cancer detection from mammograms. In this review paper a comprehensive review of ML and DL algorithms and their applications in mammogram image analysis are presented. Various supervised and unsupervised learning techniques, such as convolutional neural networks (CNNs), support vector machines (SVMs), random forests, and other popular ML and DL models are discussed in paper. The integration of these DL methods that are efficiently used in image preprocessing techniques, feature extraction, and classification strategies. The overall survey focusses on various performance metrics, datasets, and benchmarks used in existing studies. Further the strengths and limitations of different approaches used by various researchers are identified. By understating current research trends this paper aims to contribute to the ongoing development of more accurate and reliable breast cancer detection systems using advanced ML techniques.
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References
Wu W-J, Moon WK. Ultrasound breast tumor image computeraided diagnosis with texture and morphological features. Academic Radiology. 2008; 15(7):873-80. PMid:18572123. http://dx.doi.org/10.1016/j.acra.2008.01.010
Renjie L, Tao W, Zengchang Q. Classification of benign and malignant breast tumors in ultrasound images based on multiple sonographic and textural features. In: International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC 2011); 2011 Aug 26-27; Hangzhou, China. Los Alamitos: IEEE Computer Society; 2011. p. 71-4
Ali, Ebrahim Edriss Ebrahim, and Wu Zhi Feng. "Breast cancer classification using support vector machine and neural network." International Journal of Science and Research 5.3 (2016): 1-6.
Badr, Eman AE, et al. "Role of serum glypican-3 in the diagnosis and differentiation of small hepatocellular carcinoma from hepatitis-C virus cirrhosis." Alexandria Journal of Medicine 50.3 (2014): 221-226.
Octaviani, T. L., and D. Z. Rustam. "Random forest for breast cancer prediction." AIP Conference Proceedings. Vol. 2168. No. 1. AIP Publishing, 2019.
Nguyen, Cuong, Yong Wang, and Ha Nam Nguyen. "Random forest classifier combined with feature selection for breast cancer diagnosis and prognostic." (2013).
Wang, Sutong, et al. "An improved random forest-based rule extraction method for breast cancer diagnosis." Applied Soft Computing 86 (2020): 105941.
Elgedawy, Madeeh Nayer. "Prediction of breast cancer using random forest, support vector machines and naïve Bayes." International Journal of Engineering and Computer Science 6.1 (2017): 19884-19889.
Rathi, Megha, and Arun Kumar Singh. "Breast cancer prediction using Naïve Bayes classifier." International Journal of Information Technology & Systems 1.2 (2012): 77-80.
Karabatak, Murat. "A new classifier for breast cancer detection based on Naïve Bayesian." Measurement 72 (2015): 32-36.
Güzel, Ceren, et al. "Breast cancer diagnosis based on naïve Bayes machine learning classifier with KNN missing data imputation." AWERProcedia Information Technology & Computer Science 4 (2013): 401-407.
Islam, Md Milon, et al. "Prediction of breast cancer using support vector machine and K-Nearest neighbors." 2017 IEEE region 10 humanitarian technology conference (R10-HTC). IEEE, 2017.
Ahn, Jong Seok, et al. "Artificial intelligence in breast cancer diagnosis and personalized medicine." Journal of Breast Cancer 26.5 (2023): 405.
Nassif, Ali Bou, et al. "Breast cancer detection using artificial intelligence techniques: A systematic literature review." Artificial intelligence in medicine 127 (2022): 102276.
Pertuz, Said, et al. "Saliency of breast lesions in breast cancer detection using artificial intelligence." Scientific Reports 13.1 (2023): 20545.
Trivedi, Anjali, and Hasina Outtz Reed. "The lymphatic vasculature in lung function and respiratory disease." Frontiers in Medicine 10 (2023): 1118583
Norton, Larry, and Roger Herdman, eds. "Saving Women's Lives: Strategies for Improving Breast Cancer Detection and Diagnosis: A Breast Cancer Research Foundation and Institute of Medicine Symposium." (2004).
A Benign and Malignant Breast Tumor Classification Method via Efficiently Combining Texture and Morphological Features on Ultrasound Images
https://www.nationalbreastcancer.org/breast-cancer-biopsy/
https://www.nibib.nih.gov/science-education/science-topics/mammography
Golshan, M., et al. "Prediction of breast cancer size by ultrasound, mammography and core biopsy." The Breast 13.4 (2004): 265-271.
Nelson, Heidi D., et al. "Factors associated with rates of false-positive and false-negative results from digital mammography screening: an analysis of registry data." Annals of internal medicine 164.4 (2016): 226-235.
Rocha, Anderson, et al. "Points of interest and visual dictionaries for automatic retinal lesion detection." IEEE transactions on biomedical engineering 59.8 (2012): 2244-2253.
Singh, Vivek Kumar, et al. "Breast tumor segmentation and shape classification in mammograms using generative adversarial and convolutional neural network." Expert Systems with Applications 139 (2020): 112855.
Liu, Renyi, et al. "A deep learning–machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors." European Radiology 32.2 (2022): 1371-1383.
Jordan, Michael I., and Tom M. Mitchell. "Machine learning: Trends, perspectives, and prospects." Science 349.6245 (2015): 255-260.
Min, Seonwoo, Byunghan Lee, and Sungroh Yoon. "Deep learning in bioinformatics." Briefings in bioinformatics 18.5 (2017): 851-869.
Kar, Arpan Kumar. "Bio inspired computing–a review of algorithms and scope of applications." Expert Systems with Applications 59 (2016): 20-32.
Holzinger, Andreas, et al. "Causability and explainability of artificial intelligence in medicine." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 9.4 (2019): e1312.
Didona, Diego, and Paolo Romano. "Hybrid machine learning/analytical models for performance prediction: A tutorial." Proceedings of the 6th acm/spec international conference on performance engineering. 2015.
Mallik, Manjarini, Ayan Kumar Panja, and Chandreyee Chowdhury. "Paving the way with machine learning for seamless indoor–outdoor positioning: A survey." Information Fusion 94 (2023): 126-151.
Charbuty, Bahzad, and Adnan Abdulazeez. "Classification based on decision tree algorithm for machine learning." Journal of Applied Science and Technology Trends 2.01 (2021): 20-28.
Taheri, Sona, and Musa Mammadov. "Learning the naive Bayes classifier with optimization models." International Journal of Applied Mathematics and Computer Science 23.4 (2013): 787-795.
Hill, Tim, et al. "Artificial neural network models for forecasting and decision making." International journal of forecasting 10.1 (1994): 5-15.
LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. "Deep learning." nature 521.7553 (2015): 436-444.
Dutta, Sourav. "An overview on the evolution and adoption of deep learning applications used in the industry." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 8.4 (2018): e1257.
Li, Zewen, et al. "A survey of convolutional neural networks: analysis, applications, and prospects." IEEE transactions on neural networks and learning systems 33.12 (2021): 6999-7019.
Salehinejad, Hojjat, et al. "Recent advances in recurrent neural networks." arXiv preprint arXiv:1801.01078 (2017).
Graves, Alex, and Alex Graves. "Long short-term memory." Supervised sequence labelling with recurrent neural networks (2012): 37-45.
Goodfellow, Ian, et al. "Generative adversarial networks." Communications of the ACM 63.11 (2020): 139-144.
Dwivedi, Vijay Prakash, and Xavier Bresson. "A generalization of transformer networks to graphs." arXiv preprint arXiv:2012.09699 (2020).
Bank, Dor, Noam Koenigstein, and Raja Giryes. "Autoencoders." Machine learning for data science handbook: data mining and knowledge discovery handbook (2023): 353-374.
Keyvanrad, Mohammad Ali, and Mohammad Mehdi Homayounpour. "A brief survey on deep belief networks and introducing a new object oriented toolbox (DeeBNet)." arXiv preprint arXiv:1408.3264 (2014).
Wolf, Peter, et al. "Learning how to drive in a real world simulation with deep q-networks." 2017 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2017.
Doersch, Carl. "Tutorial on variational autoencoders." arXiv preprint arXiv:1606.05908 (2016).
Wu, Zonghan, et al. "A comprehensive survey on graph neural networks." IEEE transactions on neural networks and learning systems 32.1 (2020): 4-24.
Kalyani Ghuge and Dr. D. Saravanan, "SRMADNet: Swin ResUnet3+-based mammogram image segmentation and heuristic adopted multi-scale attention based DenseNet for breast cancer detection," Biomedical Signal Processing and Control, vol.88, pp.105515, 2023.
S. R. Sannasi Chakravarthy, N. Bharanidharan and Harikumar Rajaguru, "Processing of digital mammogram images using optimized ELM with deep transfer learning for breast cancer diagnosis," Multimedia Tools and Applications, May 2023.
T. Kavitha, Paul P. Mathai, C. Karthikeyan, M. Ashok, Rachna Kohar, J. Avanija and S. Neelakandan, "Deep Learning Based Capsule Neural Network Model for Breast Cancer Diagnosis Using Mammogram Images," Interdisciplinary Sciences: Computational Life Sciences, vol.14, pp.113–129, 2022.
Saida Sarra Boudouh and Mustapha Bouakkaz, "New enhanced breast tumor detection approach in mammogram scans based on pre-processing and deep transfer learning techniques," Multimedia Tools and Applications, September 2023.
L. Kanya Kumari and B. Naga Jagadesh, "An adaptive teaching learning based optimization technique for feature selection to classify mammogram medical images in breast cancer detection," International Journal of System Assurance Engineering and Management, January 2022.
Prabhpreet Kaur, Gurvinder Singh and Parminder Kaur," Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification," Informatics in Medicine Unlocked, vol. 16, pp.100151, 2019.
Ayman Altameem, Chandrakanta Mahanty, Ramesh Chandra Poonia, Abdul Khader Jilani Saudagar, and Raghvendra Kumar," Breast Cancer Detection in Mammography Images Using Deep Convolutional Neural Networks and Fuzzy Ensemble Modeling Techniques," Diagnostics, vol.12, Issue. 8, May 2022.
E. L. Omonigho, M. David, A. Adejo and S. Aliyu, "Breast Cancer:Tumor Detection in Mammogram Images Using Modified AlexNet Deep Convolution Neural Network," Computer Engineering and Computer Science (ICMCECS), pp. 1-6, 2020.
Chaitanya Singla, Pradeepta Kumar Sarangi, Ashok Kumar Sahoo and Pramod Kumar Singh, "Deep learning enhancement on mammogram images for breast cancer detection," Materials Today: Proceedings, vol.49, Part 8, pp.3098-3104, 2022.
Ahn, Jong Seok, et al. "Artificial intelligence in breast cancer diagnosis and personalized medicine." Journal of Breast Cancer 26.5 (2023): 405.
Nassif, Ali Bou, et al. "Breast cancer detection using artificial intelligence techniques: A systematic literature review." Artificial intelligence in medicine 127 (2022): 102276.
Muramatsu, Chisako, et al. "Effect of reference image retrieval on breast mass classification performance: ROC analysis." Breast Image Analysis MICCAI (2013): 50-57.
Chokri, Ferkous, and Merouani Hayet Farida. "Mammographic mass classification according to Bi‐RADS lexicon." IET Computer Vision 11.3 (2017): 189-198.
Li, Dan, et al. "Analysis of MiR-195 and MiR-497 expression, regulation and role in breast cancer." Clinical cancer research 17.7 (2011): 1722-1730.
Beura, Shradhananda, Banshidhar Majhi, and Ratnakar Dash. "Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer." Neurocomputing 154 (2015): 1-14.
Rouhi, Rahimeh, and Mehdi Jafari. "Classification of benign and malignant breast tumors based on hybrid level set segmentation." Expert Systems with Applications 46 (2016): 45-59.