Abstract
Breast cancer is one of the type of cancer that appears most commonly in women. Mortality of women who are suffering from breast cancer is very high so it is required to detect the cancer in the very early stage and treat the breast cancer patients to save their life. This research helps the radiologist to identify the mammogram image as normal or abnormal. We have applied Convolution neural networks and built different models with different combinations of layers for the purpose of identifying the mammogram image as normal and abnormal. We have conducted experiments on different deep learning models such as alexnet, inception network and Visual Geometry Group 16. We have conducted experiments on raw images and on the processed images. We obtained different performance accuracy for each model on two different datasets such as Mammograms-MIAS and Curated Breast Imaging Subset of DDSM.
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Acknowledgement
This publication is resulted from research work funded and supported by the Vision Group on Science and Technology, Govt. of. Karnataka (GRD No. 782). The Grant provided me with support for the high quality equipment needed for research in healthcare applications.
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Sneha, S., Bharathi, M.A. (2022). Deep Learning Based Mammogram Image Classification. In: Chen, J.IZ., Tavares, J.M.R.S., Iliyasu, A.M., Du, KL. (eds) Second International Conference on Image Processing and Capsule Networks. ICIPCN 2021. Lecture Notes in Networks and Systems, vol 300. Springer, Cham. https://doi.org/10.1007/978-3-030-84760-9_59
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DOI: https://doi.org/10.1007/978-3-030-84760-9_59
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