Deep CNN-Based Computer-Aided Diagnosis for Drowning Detection using Post-mortem Lungs CT Images

Abstract

Drowning death rate is high in Japan and its diagnosis is still one of the most challenging tasks in the field of forensics due to the complex interpretation of its pathology. Postmortem lungs computed tomography (CT) images can be used for interpretation of forensic pathology due to its benefits but shortage of specialists is a critical problem. Also, manually interpreting CT images is a tiring and time-taking process. In this paper, we proposed a computer-aided diagnosis system based on a deep convolutional neural network (DCNN) for classifying the post-mortem lungs CT images into drowning and non-drowning. A pre-trained DCNN was implemented in this study for classification of post-mortem lungs CT images. The DCNN was trained and tested using a post-mortem lungs CT image database obtained from Tohoku University Autopsy Imaging Center. The training process involves fine-tuning. The experimental results demonstrated a receiver operating characteristic (ROC) curve and an area under the curve (AUC) of 95 percent was achieved in drowning detection using the post-mortem lungs CT images.