Determination of Causes of Death via Spectrochemical Analysis of Forensic autopsies-based Pulmonary Edema Fluid Samples with Deep Learning Algorithm.

Researchers

Journal

Modalities

Models

Abstract

This study investigated whether infrared spectroscopy combined with a deep learning algorithm could be a useful tool for determining causes of death by analyzing pulmonary edema fluid from forensic autopsies. A newly designed convolutional neural network-based deep learning framework, named DeepIR, and 8 popular machine learning algorithms, were used to construct classifiers. The prediction performances of these classifiers demonstrated that DeepIR outperformed the machine learning algorithms in establishing classifiers to determine the causes of death. Moreover, DeepIR was generally less dependent on preprocessing procedures than were the machine learning algorithms; it provided the validation accuracy with a narrow range from 0.9661-0.9856 and the test accuracy ranging from 0.8774-0.9167 on the raw pulmonary edema fluid spectral dataset and the 9 preprocessing protocol-based datasets in our study. In conclusion, this study demonstrates deep learning-equipped Fourier Transform Infrared spectroscopy technique has the potential to be an effective aid for determining causes of death. This article is protected by copyright. All rights reserved.
This article is protected by copyright. All rights reserved.