@article{ref1,
title="A deep learning aided drowning diagnosis for forensic investigations using post-mortem lung CT images",
journal="Annual International Conference of the IEEE Engineering in Medicine and Biology Society.",
year="2020",
author="Homma, Noriyasu and Zhang, Xiaoyong and Qureshi, Amber and Konno, Takuya and Kawasumi, Yusuke and Usui, Akihito and Funayama, Masato and Bukovsky, Ivo and Ichiji, Kei and Sugita, Norihiro and Yoshizawa, Makoto",
volume="2020",
number="",
pages="1262-1265",
abstract="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.<p /> <p>Language: en</p>",
language="en",
issn="2375-7477",
doi="10.1109/EMBC44109.2020.9175731",
url="http://dx.doi.org/10.1109/EMBC44109.2020.9175731"
}