Abstract
Cancer in any form is one of the most deadly illnesses in the world. Scientists are investigating into this disease and developing methods and treatments to fight it. The recent surveys show that breast cancer is also one of the major causes of mortality rate among female population around the world. Breast cancer’s definition may be explained as some old cells that aggressively grow out of control to form a population of a harmful mass in the breast tissue. Eventually, as a result they lead to the formation a malignant tumor. Deep learning (DL) that is the subfield of machine learning algorithms provides a powerful tool to help experts to analyze, model and make sense of complex clinical data across a broad range of medical applications. The aim of this study is to develop an efficient system to classify breast tumors as malignant and benign. This system is divided in two stages. The first stage is the normalization of the data. The second stage is the classification of tumors. The accuracy of the approach is 98.42%. The overall result showed that the DL outperformed the previous studies where the same data set was used.
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Ilhan, U., Uyar, K., Iseri, E.I. (2021). Breast Cancer Classification Using Deep Learning. In: Aliev, R.A., Kacprzyk, J., Pedrycz, W., Jamshidi, M., Babanli, M., Sadikoglu, F.M. (eds) 14th International Conference on Theory and Application of Fuzzy Systems and Soft Computing – ICAFS-2020 . ICAFS 2020. Advances in Intelligent Systems and Computing, vol 1306. Springer, Cham. https://doi.org/10.1007/978-3-030-64058-3_88
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DOI: https://doi.org/10.1007/978-3-030-64058-3_88
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