Research Article
Estimation of the ambit of breast cancer with a modified ResNet analysis using machine learning approach
@INPROCEEDINGS{10.4108/eai.7-6-2021.2308600, author={Narayanappa C K and Poornima G R and Basavaraj V Hiremath}, title={Estimation of the ambit of breast cancer with a modified ResNet analysis using machine learning approach}, proceedings={Proceedings of the First International Conference on Computing, Communication and Control System, I3CAC 2021, 7-8 June 2021, Bharath University, Chennai, India}, publisher={EAI}, proceedings_a={I3CAC}, year={2021}, month={6}, keywords={breast cancer classification benign malignant bi-rads m-resnet}, doi={10.4108/eai.7-6-2021.2308600} }- Narayanappa C K
Poornima G R
Basavaraj V Hiremath
Year: 2021
Estimation of the ambit of breast cancer with a modified ResNet analysis using machine learning approach
I3CAC
EAI
DOI: 10.4108/eai.7-6-2021.2308600
Abstract
Breast Cancer has been one of the most common reasons for mortality and morbidity among the females around the world especially in developing countries. In this regard, Mammography is a popular screening technique for breast cancer diagnosis so as to label the existence of cancerous cells. The present work encompasses the design and development of a M-ResNet (Modified ResNet) approach so as to classify the breast cancer into benign and malignant conditions with the inclusions for supervised classification models with the training of both upper as well as the lower layers of the designed networks. The efficacy of the developed approach was evaluated using various performance evaluators such as those of sensitivity, specificity, accuracy and F1-Score. Bi-Rads score was used as a basis for the classification process wherein a score of 0-3 correlated to benign and it is non-cancerous nature of tissues whereas malignancy was denoted by a score of 4 and above. InBreast dataset, a publicly available online dataset with 112 breast images were used for the evaluation of the developed paradigm. The present paradigm portrayed an accuracy of 96.43% with Area Under the Curve (AUC) of 95.63%.
