Comparing convolutional deep neural networks for computer-assisted diagnosis in breast cancer.
Duggento A., Scimeca M., Urbano N., Bonanno E., Aiello M., Cavaliere C., Cascella G. L., Cascella D., Conte G., Guerrisi M., Toschi N.
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V - Biofisica e fisica medica
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Novel ideas in computer-assisted diagnosis have been prompted by the introduction of deep learning techniques in general and of convolutional neural networks (CNN) in particular. We design and validate an ad hoc CNN architecture specialized in breast lesion classification and heuristically explore possible parameter combinations and architecture styles in order to propose a model selection criterion which can pose the emphasis on reducing false negatives while still retaining acceptable accuracy. We achieve good classification performance on the validation and test set, demonstrating how an ad hoc, random initialization CNN architecture can provide practical aid in the classification and staging of breast cancer.