Abstract
Currently, interpretation of medical images is almost exclusively made by specialized physicians. Although, the next decades will most certainly be of change and computer-aided diagnosis systems will play an important role in the reading process. Assisted interpretation of medical images has become one of the major research subjects in medical imaging and diagnostic radiology. From a methodological point of view, the main attraction for the resolution of this kind of problem arises from the combination of the image reading made by the radiologists, with the results obtained from using Artificial Intelligence based applications that will contribute to the reduction and eventually the elimination of perception errors. This article describes how machine learning algorithms can help distinguish normal readings in brain Computed Tomography from all its variations. The goal is to have a system that is able to detect normal appearing structures, thus identifying normal studies, making the reading by the radiologist unnecessary for a large proportion of the brain Computed Tomography scans.
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Peixoto, H., Alves, V. (2009). Computer-Aided Diagnosis in Brain Computed Tomography Screening. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2009. Lecture Notes in Computer Science(), vol 5633. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03067-3_7
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DOI: https://doi.org/10.1007/978-3-642-03067-3_7
Publisher Name: Springer, Berlin, Heidelberg
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