Abstract
Breast cancer is the most lethal cancer among women. Early-stage diagnosis may reduce the mortality associated with breast cancer subjects. Diagnosis can be made with screening mammography. The main challenge of screening mammography is its high risk of false positives and false negatives. This paper presents the detection of breast cancer in mammograms using the VGG16 model of deep learning approaches. The VGG16 model is trained and tested on 322 images from the MIAS dataset. It performs better as compared to AlexNet, EfficientNet, and GoogleNet models. Classification of mammograms will improve mammograms’ efficient screening, which will be a support system to radiologists.
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Pillai, A., Nizam, A., Joshee, M., Pinto, A., Chavan, S. (2022). Breast Cancer Detection in Mammograms Using Deep Learning. In: Iyer, B., Ghosh, D., Balas, V.E. (eds) Applied Information Processing Systems . Advances in Intelligent Systems and Computing, vol 1354. Springer, Singapore. https://doi.org/10.1007/978-981-16-2008-9_11
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DOI: https://doi.org/10.1007/978-981-16-2008-9_11
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