Citation

Homma N, Zhang X, Qureshi A, Konno T, Kawasumi Y, Usui A, Funayama M, Bukovsky I, Ichiji K, Sugita N, Yoshizawa M. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2020; 2020: 1262-1265.

Copyright

(Copyright © 2020, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/EMBC44109.2020.9175731

PMID

33018217

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.

Language: en