Abstract
Breast cancer has the second highest mortality rate in women next to lung cancer. As per clinical statistics, 1 in every 8 women is diagnosed with breast cancer in their lifetime. However, periodic clinical checkups and self-tests help in early detection and thereby significantly increase the chances of survival. Invasive detection techniques cause rupture of the tumor, accelerating the spread of cancer to adjoining areas. Hence, there arises the need for a more robust, fast, accurate, and efficient noninvasive cancer detection system. In this work, an automated system is proposed for achieving error-free detection of breast cancer using mammogram. In this system, the deep learning techniques such as convolutional neural network, sparse autoencoder, and stacked sparse autoencoder are used. The performance of these techniques is analyzed and compared with the existing methods. From the analysis, it is observed that the stacked sparse autoencoder performs better compared to other methods.
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Selvathi, D., Aarthy Poornila, A. (2018). Deep Learning Techniques for Breast Cancer Detection Using Medical Image Analysis. In: Hemanth, J., Balas , V. (eds) Biologically Rationalized Computing Techniques For Image Processing Applications. Lecture Notes in Computational Vision and Biomechanics, vol 25. Springer, Cham. https://doi.org/10.1007/978-3-319-61316-1_8
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DOI: https://doi.org/10.1007/978-3-319-61316-1_8
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