A Deep Learning Aided Drowning Diagnosis for Forensic Investigations using Post-Mortem Lung CT Images

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F20%3A00344449" target="_blank" >RIV/68407700:21220/20:00344449 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/EMBC44109.2020.9175731" target="_blank" >https://doi.org/10.1109/EMBC44109.2020.9175731</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/EMBC44109.2020.9175731" target="_blank" >10.1109/EMBC44109.2020.9175731</a>

Result language
angličtina
Original language name
A Deep Learning Aided Drowning Diagnosis for Forensic Investigations using Post-Mortem Lung CT Images
Original language description
Feasibility of computer-aided diagnosis (CAD) systems has been demonstrated in the field of medical image diagnosis. Especially, deep learning based CAD systems showed high performance thanks to its capability of image recognition. However, there is no CAD system developed for post-mortem imaging diagnosis and thus it is still unclear if the CAD system is effective for this purpose. Particulally, the drowning diagnosis is one of the most difficult tasks in the field of forensic medicine because findings of the post-mortem image diagnosis are not specific. To address this issue, we develop a CAD system consisting of a deep convolution neural network (DCNN) to classify post-mortem lung computed tomography (CT) images into two categories of drowning and non-drowning cases. The DCNN was trained by means of transfer learning and performance evaluation was conducted by 10-fold cross validation using 140 drowning cases and 140 non-drowning cases of the CT images. The area under the receiver operating characteristic curve (AUC-ROC) for the DCNN was achieved 0.88 in average. This high performance clearly demonstrated that the proposed DCNN based CAD system has a potential for post-mortem image diagnosis of drowning.
Czech name
—
Czech description
—

Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
ISBN
9781728119908
ISSN
—
e-ISSN
1558-4615
Number of pages
4
Pages from-to
1262-1265
Publisher name
IEEE
Place of publication
Montreal
Event location
Montreal
Event date
Jul 20, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000621592201144

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