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https://www.selleckchem.com/products/pf-03084014-pf-3084014.html Thus, even if the material for toxicological tests is handled properly, detection of the presence of helium in a relatively short period of time after death is usually impossible or very difficult. If death due to inert gas inhalation is suspected during an autopsy, samples of biological material can be collected to be tested later by gas chromatography combined with mass spectrometry (GC-MS), but the results of the investigations are usually not helpful from the point of view of a forensic pathologist.Forensic Odontology deals with identifying humans based on their dental traits because of their robust nature. Classical methods of human identification require more manual effort and are difficult to use for large number of Images. A Novel way of automating the process of human identification by using deep learning approaches is proposed in this paper. Transfer learning using AlexNet is applied in three stages In the first stage, the features of the query tooth image are extracted and its location is identified as either in the upper or lower Jaw. In the second stage of transfer learning, the tooth is then classified into any of the four classes namely Molar, Premolar, Canine or Incisor. In the last stage, the classified tooth is then numbered according to the universal numbering system and finally the candidate identification is made by using distance as metrics. These three stage transfer learning approach proposed in this work helps in reducing the search space in the process of candidate matching. Also, instead of making the network classify all the 32 teeth into 32 different classes, this approach reduces the number of classes assigned to the classification layer in each stage thereby increasing the performance of the network. This work outperforms the classical approaches in terms of both accuracy and precision. The hit rate in human identification is also higher compared to the other state-of-art

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