Blood Stain Classification with Hyperspectral Imaging and Deep Neural Networks
| Autor: | Bartosz Grabowski, Michał Romaszewski, Michał Cholewa, Kamil Książek, Przemysław Głomb |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Support Vector Machine
Computer science forensic science 0211 other engineering and technologies 02 engineering and technology lcsh:Chemical technology 01 natural sciences Biochemistry Convolutional neural network Article Analytical Chemistry convolutional neural networks Humans lcsh:TP1-1185 Electrical and Electronic Engineering Instrumentation 021101 geological & geomatics engineering Artificial neural network business.industry Deep learning 010401 analytical chemistry Confusion matrix Hyperspectral imaging deep learning Pattern recognition Hyperspectral Imaging Forensic Medicine Atomic and Molecular Physics and Optics 0104 chemical sciences Support vector machine Recurrent neural network Blood Stains deep neural networks Multilayer perceptron recurrent neural network Artificial intelligence Neural Networks Computer business hyperspectral classification |
| Zdroj: | Sensors Volume 20 Issue 22 Sensors (Basel, Switzerland) Sensors, Vol 20, Iss 6666, p 6666 (2020) |
| ISSN: | 1424-8220 |
| DOI: | 10.3390/s20226666 |
| Popis: | In recent years, growing interest in deep learning neural networks has raised a question on how they can be used for effective processing of high-dimensional datasets produced by hyperspectral imaging (HSI). HSI, traditionally viewed as being within the scope of remote sensing, is used in non-invasive substance classification. One of the areas of potential application is forensic science, where substance classification on the scenes is important. An example problem from that area&mdash blood stain classification&mdash is a case study for the evaluation of methods that process hyperspectral data. To investigate the deep learning classification performance for this problem we have performed experiments on a dataset which has not been previously tested using this kind of model. This dataset consists of several images with blood and blood-like substances like ketchup, tomato concentrate, artificial blood, etc. To test both the classic approach to hyperspectral classification and a more realistic application-oriented scenario, we have prepared two different sets of experiments. In the first one, Hyperspectral Transductive Classification (HTC), both a training and a test set come from the same image. In the second one, Hyperspectral Inductive Classification (HIC), a test set is derived from a different image, which is more challenging for classifiers but more useful from the point of view of forensic investigators. We conducted the study using several architectures like 1D, 2D and 3D convolutional neural networks (CNN), a recurrent neural network (RNN) and a multilayer perceptron (MLP). The performance of the models was compared with baseline results of Support Vector Machine (SVM). We have also presented a model evaluation method based on t-SNE and confusion matrix analysis that allows us to detect and eliminate some cases of model undertraining. Our results show that in the transductive case, all models, including the MLP and the SVM, have comparative performance, with no clear advantage of deep learning models. The Overall Accuracy range across all models is 98&ndash 100% for the easier image set, and 74&ndash 94% for the more difficult one. However, in a more challenging inductive case, selected deep learning architectures offer a significant advantage their best Overall Accuracy is in the range of 57&ndash 71%, improving the baseline set by the non-deep models by up to 9 percentage points. We have presented a detailed analysis of results and a discussion, including a summary of conclusions for each tested architecture. An analysis of per-class errors shows that the score for each class is highly model-dependent. Considering this and the fact that the best performing models come from two different architecture families (3D CNN and RNN), our results suggest that tailoring the deep neural network architecture to hyperspectral data is still an open problem. |
| Databáze: | OpenAIRE |
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