DRDarkNet: a hybrid deep feature engineering model for accurate autopsy image classification

Abstract

In deaths due to injury, photographs of changes on deceased bodies are routinely taken during the forensic examination; the task of differentiating the types of fatal injury can be posed as an image classification problem. We aimed to develop a machine learning model for automated classification of the cause of injury-induced deaths based on postmortem images of external body regions. We collected a dataset comprising 4254 autopsy images of various body parts divided into six classes according to the cause of death: (i) crush (1808), (ii) choking (327), (iii) stabbing (977), (iv) gunshot (765), (v) burns (254), and (vi) drowning (127). Our model, DRDarkNet, comprised four phases: feature extraction; feature selection; classification; and information fusion. DenseNet201, ResNet50, and DarkNet53 pre-trained on the ImageNet-1 K dataset were deployed to generate six feature vectors of different lengths using the fully connected and global average pooling layers of the individual networks. Neighborhood component analysis (NCA), Chi2, and ReliefF functions were used to create 18 (= 6 & times; 3) selected feature vectors of identical length (512) with reduced dimensionality that contained the most discriminative features. These selected feature vectors were then fed to a support vector machine classifier to generate 18 classifier-wise outputs. Novel pruning-based iterative majority voting (PIMV) was used to aggregate the classifier-wise outputs, from which voted outputs were generated. From both classifier-wise and voted outputs, the most accurate output was automatically chosen, rendering the model self-organized. DRDarkNet outputs both classifier-wise results and voted results, attaining an excellent 96.47% overall multiclass classification accuracy.