Abstract
Deep learning comes under machine learning. It includes statistics and predictive modeling, which plays vital role in data science. It helps in acquiring and analyzing vast amount of data quick and easier. This technique is employed in image recognition tools and natural language processing. Carcinoma is one other frequently occurring cancer in women. Carcinoma can be identified in two variants: One is benign, and another one is malignant. Automatic detection in medical imaging has become the vital field in many medical diagnostic applications. Automated detection of breast cancer in magnetic resonance imaging (MRI), and mammography is very crucial as it provides information about breast lesions. Human inspection is the conventional method for defect detection in magnetic resonance images. This method is impractical for large amount of data. So, cancer detection methods are developed as it would save radiologist time and also the risk faced by woman. Various machine learning algorithms are used to identify breast cancer. Deep learning models have been widely used in the classification of medical images. To improvise the accuracy in the model various, deep learning approaches are to be used to detect the breast cancer. The proposed approach classifies the breast cancer not just as benign or malignant, but it will classify the subclasses of breast cancer. They are Benign, Lobular Carcinoma, Mucinous Carcinoma, Ductal Carcinoma, and Papillary Carcinoma. To classify the subclasses of tumor, we use DenseNet Architecture. Image preprocessing is done using histogram equalization method.
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Selvaraj, S., Deepa, D., Ramya, S., Priya, R., Ramya, C., Ramya, P. (2023). Classification of Breast Cancer Using CNN and Its Variant. In: Rajakumar, G., Du, KL., Vuppalapati, C., Beligiannis, G.N. (eds) Intelligent Communication Technologies and Virtual Mobile Networks. Lecture Notes on Data Engineering and Communications Technologies, vol 131. Springer, Singapore. https://doi.org/10.1007/978-981-19-1844-5_3
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DOI: https://doi.org/10.1007/978-981-19-1844-5_3
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