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.


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Breast Cancer, MRI