Various Deep Learning Techniques Involved In Breast Cancer Mammogram Classification – A Survey
DOI:
https://doi.org/10.59615/ijie.2.4.41DOR:
https://dorl.net/dor/20.1001.1.27831906.2022.2.4.4.9Keywords:
Mammogram, Breast Cancer, Support Vector Machine, Deep Learning, CNNAbstract
The most common and rapidly spreading disease in the world is breast cancer. Most cases of breast cancer are observed in females. Breast cancer can be controlled with early detection. Early discovery helps to manage a lot of cases and lower the death rate. On breast cancer, numerous studies have been conducted. Machine learning is the method that is utilized in research the most frequently. There have been a lot of earlier machine learning-based studies. Decision trees, KNN, SVM, naive bays, and other machine learning algorithms perform better in their respective fields. However, a newly created method is now being utilized to categorize breast cancer. Deep learning is a recently developed method. The limitations of machine learning are solved through deep learning. Convolution neural networks, recurrent neural networks, deep belief networks, and other deep learning techniques are frequently utilized in data science. Deep learning algorithms perform better than machine learning algorithms. The best aspects of the images are extracted. CNN is employed in our study to categorize the photos. Basically, CNN is the most widely used technique to categorize images, on which our research is based.
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• Aslan, M. F., Celik, Y., Sabancı, K., & Durdu, A. (2018). Breast cancer diagnosis by different machine learning methods using blood analysis data. International Journal of Intelligent Systems and Applications in Engineering.
• Bayrak, E. A., Kırcı, P., & Ensari, T. (2019, April). Comparison of machine learning methods for breast cancer diagnosis. In 2019 Scientific meeting on electrical-electronics & biomedical engineering and computer science (EBBT) (pp. 1-3). IEEE.
• Bhowmik, C., Ghantasala, G. P., & AnuRadha, R. (2021). A Comparison of Various Data Mining Algorithms to Distinguish Mammogram Calcification Using Computer-Aided Testing Tools. In Proceedings of the Second International Conference on Information Management and Machine Intelligence (pp. 537-546). Springer, Singapore.
• CADe, M. (2020). CADx for Identifying Microcalcification Using Support Vector Machine. Journal of Communication Engineering & Systems, 10(2), 9-16p.
• Chandana, P., Ghantasala, G. P., Jeny, J. R. V., Sekaran, K., Deepika, N., Nam, Y., & Kadry, S. (2020). An effective identification of crop diseases using faster region based convolutional neural network and expert systems. International Journal of Electrical and Computer Engineering (IJECE), 10(6), 6531-6540.
• Chatterjee, C. C., & Krishna, G. (2019, September). A novel method for IDC prediction in breast cancer histopathology images using deep residual neural networks. In 2019 2nd International Conference on Intelligent Communication and Computational Techniques (ICCT) (pp. 95-100). IEEE.
• Deshwal, V., & Sharma, M. (2019). Breast Cancer Detection using SVM Classifier with Grid Search Technique. International Journal of Computer Applications, 975, 8887.
• Gadde, S. S., Anand, D., Sasidhar Babu, N., Pujitha, B. V., Sai Reethi, M., & Pradeep Ghantasala, G. S. (2022). Performance Prediction of Students Using Machine Learning Algorithms. In Applications of Computational Methods in Manufacturing and Product Design (pp. 405-411). Springer, Singapore.
• Ghantasala, G. P., & Kumari, N. V. (2021). Breast Cancer Treatment Using Automated Robot Support Technology For Mri Breast Biopsy. INTERNATIONAL JOURNAL OF EDUCATION, SOCIAL SCIENCES AND LINGUISTICS, 1(2), 235-242.
• Ghantasala, G. P., & Kumari, N. V. (2021a). Identification of Normal and Abnormal Mammographic Images Using Deep Neural Network. Asian Journal For Convergence In Technology (AJCT), 7(1), 71-74.
• Ghantasala, G. P., Kallam, S., Kumari, N. V., & Patan, R. (2020a, March). Texture Recognization and Image Smoothing for Microcalcification and Mass Detection in Abnormal Region. In 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA) (pp. 1-6). IEEE.
• Ghantasala, G. P., Kumari, N. V., & Patan, R. (2021d). Cancer prediction and diagnosis hinged on HCML in IOMT environment. In Machine Learning and the Internet of Medical Things in Healthcare (pp. 179-207). Academic Press.
• Ghantasala, G. P., Rao, D. N., & Mandal, K. (2021b). MACHINE LEARNING ALGORITHMS BASED BREAST CANCER PREDICTION MODEL. Journal of Cardiovascular Disease Research, 12(4), 50-56.
• Ghantasala, G. P., Reddy, A. R., & Arvindhan, M. (2021c). Prediction of Coronavirus (COVID-19) Disease Health Monitoring with Clinical Support System and Its Objectives. In Machine Learning and Analytics in Healthcare Systems (pp. 237-260). CRC Press.
• Ghantasala, G. P., Reddy, A. R., & Ayyappa, R. M. K. (2022). Protecting Patient Data with 2F-Authentication. Cognitive Intelligence and Big Data in Healthcare, 169.
• Ghantasala, G. P., Reddy, A., Peyyala, S., & Rao, D. N. (2021a). Breast Cancer Prediction In Virtue Of Big Data Analytics. INTERNATIONAL JOURNAL OF EDUCATION, SOCIAL SCIENCES AND LINGUISTICS, 1(1), 130-136.
• Ghantasala, G. P., Tanuja, B., Teja, G. S., & Abhilash, A. S. (2020b). Feature Extraction and Evaluation of Colon Cancer using PCA, LDA and Gene Expression. Forest, 10(98), 99.
• Kishore, D. R., Syeda, N., Suneetha, D., Kumari, C. S., & Ghantasala, G. P. (2021). Multi Scale Image Fusion through Laplacian Pyramid and Deep Learning on Thermal Images. Annals of the Romanian Society for Cell Biology, 3728-3734.
• Krishna, N. M., Sekaran, K., Vamsi, A. V. N., Ghantasala, G. P., Chandana, P., Kadry, S., ... & Damaševičius, R. (2019). An efficient mixture model approach in brain-machine interface systems for extracting the psychological status of mentally impaired persons using EEG signals. IEEE Access, 7, 77905-77914.
• Kumari, N. V., & Ghantasala, G. P. (2020). Support Vector Machine Based Supervised Machine Learning Algorithm for Finding ROC and LDA Region. Journal of Operating Systems Development & Trends, 7(1), 26-33.
• Kwekha-Rashid, A. S., Abduljabbar, H. N., & Alhayani, B. (2021). Coronavirus disease (COVID-19) cases analysis using machine-learning applications. Applied Nanoscience, 1-13.
• Mandal, K., & Ghantasala, G. P. (2019). A complete survey on technological challenges of iot in security and privacy. Int. J. Recent Technol. Eng., 7(6S4), 332-334.
• Mandal, K., Ghantasala, G. P., Khan, F., Sathiyaraj, R., & Balamurugan, B. (2020). Futurity of Translation Algorithms for Neural Machine Translation (NMT) and Its Vision. In Natural Language Processing in Artificial Intelligence (pp. 53-95). Apple Academic Press.
• Mathew, T. E. (2019). Simple and ensemble decision tree classifier based detection of breast cancer. International Journal of Scientific & Technology Research, 8(11), 1628-1637.
• Medjahed, S. A., Saadi, T. A., & Benyettou, A. (2015). Urinary system diseases diagnosis using machine learning techniques. International Journal of Intelligent Systems and Applications, 7(5), 1.
• Murugan, B. S. (2010). Classification of Breast Cancer Using SVM Classifier Technique. International Journal of Advanced Research in Computer Science, 1(4).
• Nguyen, C. P., Vo, A. H., & Nguyen, B. T. (2019, September). Breast cancer histology image classification using deep learning. In 2019 19th international symposium on communications and information technologies (ISCIT) (pp. 366-370). IEEE.
• Nozari, H., & Sadeghi, M. E. (2021). Artificial intelligence and Machine Learning for Real-world problems (A survey). International Journal of Innovation in Engineering, 1(3), 38-47.
• Nozari, H., Szmelter-Jarosz, A., & Ghahremani-Nahr, J. (2022). Analysis of the Challenges of Artificial Intelligence of Things (AIoT) for the Smart Supply Chain (Case Study: FMCG Industries). Sensors, 22(8), 2931.
• Patan, R., Ghantasala, G. P., Sekaran, R., Gupta, D., & Ramachandran, M. (2020). Smart healthcare and quality of service in IoT using grey filter convolutional based cyber physical system. Sustainable Cities and Society, 59, 102141.
• Pradeep Ghantasala, G. S., Nageswara Rao, D., & Patan, R. (2022). Recognition of Dubious Tissue by Using Supervised Machine Learning Strategy. In Applications of Computational Methods in Manufacturing and Product Design (pp. 395-404). Springer, Singapore.
• Priyanka, K. S. (2021). A review paper on breast cancer detection using deep learning. In IOP conference series: materials science and engineering (Vol. 1022, No. 1, p. 012071). IOP Publishing.
• Rampun, A., Scotney, B. W., Morrow, P. J., & Wang, H. (2018, September). Breast mass classification in mammograms using ensemble convolutional neural networks. In 2018 IEEE 20th International Conference on e-Health Networking, Applications and Services (Healthcom) (pp. 1-6). IEEE.
• Rathi, M., & Pareek, V. (2016). Hybrid approach to predict breast cancer using machine learning techniques. International Journal of Computer Science Engineering, 5(3), 125-136.
• Reddy, A. R., Ghantasala, G. S., Patan, R., Manikandan, R., & Kallam, S. (2021). Smart Assistance of Elderly Individuals in Emergency Situations at Home. In Internet of Medical Things (pp. 95-115). Springer, Cham.
• Reddy, A., Maheswari, M. U., Viswanathan, A., & Vikram, G. (2022). Using Support Vector Machine For Classification And Feature Extraction Of Spam In Email. International Journal of Innovation in Engineering, 2(2), 26-32.
• Rupa, C., MidhunChakkarvarthy, D., Patan, R., Prakash, A. B., & Pradeep, G. G. (2022). Knowledge engineering–based DApp using blockchain technology for protract medical certificates privacy. IET Communications.
• Sharma, O. (2019, February). A new activation function for deep neural network. In 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon) (pp. 84-86). IEEE.
• Sharma, O. (2019, February). Deep challenges associated with deep learning. In 2019 international conference on machine learning, big data, cloud and parallel computing (COMITCon) (pp. 72-75). IEEE.
• Sharma, O. (2019, November). A Novel Activation Function in Convolutional Neural Network for Image Classification in Deep Learning. In International Conference on Recent Developments in Science, Engineering and Technology (pp. 120-130). Springer, Singapore.
• Sharma, O., & Batra, N. (2020). Optimizing Text Data in Deep Learning: An Experimental Approach. In Big Data, IoT, and Machine Learning (pp. 133-150). CRC Press.
• Sharma, R., Mehta, K., & Sharma, O. (2021, December). Exploring Deep Learning to Determine the Optimal Environment for Stock Prediction Analysis. In 2021 International Conference on Computational Performance Evaluation (ComPE) (pp. 148-152). IEEE.
• Shravya, C., Pravalika, K., & Subhani, S. (2019). Prediction of breast cancer using supervised machine learning techniques. International Journal of Innovative Technology and Exploring Engineering (IJITEE), 8(6), 1106-1110.
• Sornam, M., Muthusubash, K., & Vanitha, V. (2017, December). A survey on image classification and activity recognition using deep convolutional neural network architecture. In 2017 ninth international conference on advanced computing (ICoAC) (pp. 121-126). IEEE.
• Sreehari, E., & Ghantasala, P. G. (2019). Climate Changes Prediction Using Simple Linear Regression. Journal of Computational and Theoretical Nanoscience, 16(2), 655-658.
• Tahmooresi, M., Afshar, A., Rad, B. B., Nowshath, K. B., & Bamiah, M. A. (2018). Early detection of breast cancer using machine learning techniques. Journal of Telecommunication, Electronic and Computer Engineering (JTEC), 10(3-2), 21-27.
• Vijayan, V. S., & Lekshmy, P. L. (2019). Deep learning based prediction of breast cancer in histopathological images. International Journal of Engineering Research & Technology (IJERT), 2278-0181.
• Xiao, Y., Wu, J., Lin, Z., & Zhao, X. (2018, July). Breast cancer diagnosis using an unsupervised feature extraction algorithm based on deep learning. In 2018 37th Chinese Control Conference (CCC) (pp. 9428-9433). IEEE.
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Copyright (c) 2022 Nageswara Rao Dronamraju
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