Abstract
Due to the high spread of the COVID-19 virus, several diagnosis support systems are being developed in order to detect the disease in a faster and accurate way. In this paper, a stacking method for Computed Tomography (CT) scans has been implemented for the pre-processing step. The method combines both slice normalization and lung segmentation in a single output image using RGB color channels, providing more information from the input slices to the CNN models. The binary classification step starts with a slice-level prediction, which applies fine-tuning to the whole model and dense layers are changed by a custom scheme to improve the performance. Then, a patient-level prediction is performed by fixing a threshold percentage of COVID positive slices that allows to make the final prediction, classifying patients as COVID or NORMAL. The accuracy and metrics obtained show the robustness of the presented method in comparison to using the normalised slices or the masks independently. Given the results obtained, the proposed method can accurately detect the COVID-19 disease and the fusion of information improves the results obtained.
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Toledano Pavón, J., Morales Vega, J.C., Carrillo-Perez, F., Herrera, L.J., Rojas, I. (2021). COVID-19 Detection Method from Chest CT Scans via the Fusion of Slice Information and Lung Segmentation. In: Rojas, I., Castillo-Secilla, D., Herrera, L.J., Pomares, H. (eds) Bioengineering and Biomedical Signal and Image Processing. BIOMESIP 2021. Lecture Notes in Computer Science(), vol 12940. Springer, Cham. https://doi.org/10.1007/978-3-030-88163-4_15
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DOI: https://doi.org/10.1007/978-3-030-88163-4_15
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