Computer-aided detection systems to improve lung cancer early diagnosis: state-of-the-art and challenges

A Traverso, E Lopez Torres, M E Fantacci, P Cerello
<span title="">2017</span> <i title="IOP Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wxgp7pobnrfetfizidmpebi4qy" style="color: black;">Journal of Physics, Conference Series</a> </i> &nbsp;
Lung cancer is one of the most lethal types of cancer, because its early diagnosis is not good enough. In fact, the detection of pulmonary nodule, potential lung cancers, in Computed Tomography scans is a very challenging and time-consuming task for radiologists. To support radiologists, researchers have developed Computer-Aided Diagnosis (CAD) systems for the automated detection of pulmonary nodules in chest Computed Tomography scans. Despite the high level of technological developments and
more &raquo; ... proved benefits on the overall detection performance, the usage of Computer-Aided Diagnosis in clinical practice is far from being a common procedure. In this paper we investigate the causes underlying this discrepancy and present a solution to tackle it: the M5L WEB-and Cloud-based on-demand Computer-Aided Diagnosis. In addition, we prove how the combination of traditional imaging processing techniques with state-of-art advanced classification algorithms allows to build a system whose performance could be much larger than any Computer-Aided Diagnosis developed so far. This outcome opens the possibility to use the CAD as clinical decision support for radiologists.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1088/1742-6596/841/1/012013">doi:10.1088/1742-6596/841/1/012013</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/22ywemcc5fbxvp5nkpcrqv5jpa">fatcat:22ywemcc5fbxvp5nkpcrqv5jpa</a> </span>
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