Abstract
Breast cancer is one of the most frequent fatal diseases among women around the world. Early diagnosis is paramount for easing such statistics, increasing the probability of successful treatment and cure. This paper proposes a hybrid approach composed of a convolutional neural network with a supervised classifier on the top capable of predicting eight specific cases of the breast tumor, being four of them malignant and four benign. The model employs the BreastNet convolution neural network to the task of mammogram images feature extraction, and it compares three distinct supervised-learning algorithms for classification purposes: (i) Optimum-Path Forest, (ii) Support Vector Machines (SVM) with Radial Basis Function, and (iii) SVM with a linear kernel. Moreover, since BreastNet is also capable of performing classification tasks, its results are further compared against the other three techniques. Experimental results demonstrate the robustness of the model, achieving \(86\%\) of accuracy over the public LAPIMO dataset.
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Notes
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Images are resized to \(128\times 128\) pixels.
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Acknowledgment
This study was financed by FAPESP grants 2013/07375-0, 2014/12236-1, and 2016/19403-6, and CNPq grants 307066/2017-7 and 427968/2018-6. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001.
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Passos, L.A., Santos, C., Pereira, C.R., Afonso, L.C.S., Papa, J.P. (2019). A Hybrid Approach for Breast Mass Categorization. In: Tavares, J., Natal Jorge, R. (eds) VipIMAGE 2019. VipIMAGE 2019. Lecture Notes in Computational Vision and Biomechanics, vol 34. Springer, Cham. https://doi.org/10.1007/978-3-030-32040-9_17
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DOI: https://doi.org/10.1007/978-3-030-32040-9_17
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