Abstract
According to a recent study conducted in 2016, 2.8 million women worldwide had already been diagnosed with breast cancer; moreover, the medical care of a patient with breast cancer is costly and, given the cost and value of the preservation of the health of the citizen, the prevention of breast cancer has become a priority in public health. We have seen the apparition of several techniques during the past 60 years, such as mammography, which is frequently used for breast cancer diagnosis. However, false positives of mammography can occur in which the patient is diagnosed positive by another technique. Also, the potential side effects of using mammography may encourage patients and physicians to look for other diagnostic methods. This article, present a Novel technique based on an inceptionV3 couples to k-Nearest Neighbors (InceptionV3-KNN) and a particular module that we named: “StageCancer.” These techniques succeed to classify breast cancer in four stages (T1: non-invasive breast cancer, T2: the tumor measures up to 2 cm, T3: the tumor is larger than 5 cm and T4: the full breast is cover by cancer).
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Acknowledgement
The work and the contribution were supported by the SPEV project “Smart Solutions in Ubiquitous Computing Environments”, 2019, University of Hradec Kralove, Faculty of Informatics and Management, Czech Republic.
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Mambou, S., Krejcar, O., Maresova, P., Selamat, A., Kuca, K. (2019). Novel Four Stages Classification of Breast Cancer Using Infrared Thermal Imaging and a Deep Learning Model. In: Rojas, I., Valenzuela, O., Rojas, F., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science(), vol 11466. Springer, Cham. https://doi.org/10.1007/978-3-030-17935-9_7
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DOI: https://doi.org/10.1007/978-3-030-17935-9_7
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