A Deep Learning Aided Drowning Diagnosis for Forensic Investigations using Post-Mortem Lung CT Images
| Autor: | Yusuke Kawasumi, Norihiro Sugita, Amber Qureshi, Kei Ichiji, Makoto Yoshizawa, Ivo Bukovsky, Xiaoyong Zhang, Noriyasu Homma, Akihito Usui, Masato Funayama, Takuya Konno |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Drowning Lung Artificial neural network Receiver operating characteristic business.industry Computer science Deep learning Pattern recognition Convolutional neural network 03 medical and health sciences Deep Learning 030104 developmental biology 0302 clinical medicine medicine.anatomical_structure medicine Humans Neural Networks Computer Tomography Artificial intelligence Tomography X-Ray Computed business 030217 neurology & neurosurgery |
| Zdroj: | EMBC |
| DOI: | 10.1109/embc44109.2020.9175731 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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