The Deep Learning Methodology For Improved Breast Cancer Diagnosis In MRI
||International Journal of Computer & Organization Trends (IJCOT)||
|© 2021 by IJCOT Journal|
|Volume - 11 Issue - 3
|Year of Publication : 2021|
|Authors : Zulaiha Parveen A, Mr.T.Senthil Kumar|
|DOI : 10.14445/22492593/IJCOT-V11I3P303|
MLA Style:Zulaiha Parveen A, Mr.T.Senthil Kumar "The Deep Learning Methodology For Improved Breast Cancer Diagnosis In MRI" International Journal of Computer and Organization Trends 11.3 (2021): 11-14.
APA Style:Zulaiha Parveen A, Mr.T.Senthil Kumar(2021) The Deep Learning Methodology For Improved Breast Cancer Diagnosis In MRI International Journal of Computer and Organization Trends, 11(3), 11-14.
Magnetic resonance imaging (MRI) is important to predict breast cancer. The prediction of breast cancer can be done by a deep transfer learning computer-aided diagnosis (CADx) methodology. In this work, deep learning of ResNet convolutional neural network (CNN) is proposed that are used to extract features from the image. Then it is trained on the CNN features between benign and malignant lesions. ResNet50 is a ResNet model variation that has 48 Convolution layers alongside 1 MaxPool and 1 Average Pool layer. Characterization execution was assessed utilizing the collector working trademark (ROC) bend and analyzed utilizing the DeLong test. The proposed CADx strategy for mpMRI may improve indicative execution by lessening the bogus positive rate and improving the positive prescient worth in bosom imaging translation.
 Rashmi, G. D., Lekha, A., &Bawane, N. (2015, December). Analysis of the efficiency of classification and prediction algorithms (Naïve Bayes) for Breast Cancer dataset. In 2015 International Conference on Emerging Research in Electronics, Computer Science and Technology (ICERECT) 108-113 IEEE.
 Kolay, N., &Erdo?mu?, P. (2016, April). The classification of breast cancer with Machine Learning Techniques. In 2016 Electric Electronics, Computer Science, Biomedical Engineerings` Meeting (EBBT)1-4 IEEE.
 Fan, Q., Zhu, C. J., Xiao, J. Y., Wang, B. H., Yin, L., Xu, X. L., &Rong, F. (2010, October). An application of apriori algorithm in SEER breast cancer data. In 2010 International Conference on Artificial Intelligence and Computational Intelligence 3, 114-116 IEEE.
 Zhang, X., & Sun, Y. (2018, December). Breast cancer risk prediction model based on C5. 0 algorithms for postmenopausal women. In 2018 International Conference on Security, Pattern Analysis, and Cybernetics (SPAC) 321-325 IEEE.
 Liu, L., & Deng, M. (2010, January). An evolutionary artificial neural network approach for breast cancer diagnosis. In 2010 Third International Conference on Knowledge Discovery and Data Mining 593-596 IEEE.
 Sarvestani, A. S., Safavi, A. A., Parandeh, N. M., &Salehi, M. (2010, October). Predicting breast cancer survivability using data mining techniques. In 2010 2nd International Conference on Software Technology and Engineering 2, V2-227 IEEE.
 Muthukumaran, S., &Velumani, B. (2014, March). A bi clustering approach for investigating patterns for breast cancer attributes. In 2014 International Conference on Intelligent Computing Applications 22-26. IEEE.
 Sanjay, A., Nair, H. V., Murali, S., & Krishnaveni, K. S. (2018, September). A data mining model to predict breast cancer using improved feature selection method on real-time data. In 2018 international conference on advances in computing, communications, and informatics (ICACCI) 2437-2440 IEEE.
 Sinha, A., Sahoo, B., Rautaray, S. S., & Pandey, M. (2019, May). Improved framework for breast cancer prediction using frequent item sets mining for attributes filtering. In 2019 International Conference on Intelligent Computing and Control Systems (ICCS) 979-982. IEEE.
 Shen, R., Yang, Y., & Shao, F. (2014, August). Intelligent breast cancer prediction model using data mining techniques. In 2014 Sixth International Conference on Intelligent Human-Machine Systems and Cybernetics 1, pp. 384-387 IEEE.
Breast Cancer, MRI