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Dense Mesh RCNN: assessment of human skin burn and burn depth severity

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Abstract

Accurate assessment and classification of burn severity have gained research interest in burn management, and delays in treatment increase the risk of human lives. Many research techniques are attempted earlier that examine the burn features and determine the depth of injuries. However, the differences like shape, color, appearance, and area lead to inaccurate separation of healthy and burn portions. At the end, we propose a Dense Mesh Region Convolutional Neural Network (RCNN) model for burn region segmentation and improving the calculation of total burn surface area (TBSA). The main objective is the precise quad mesh prediction that gives the full 3D human shape representation and models fine structures of each detected object from a diverse, real-world environment. In this model, we have introduced the shape alignment module preserves the feature correspondence between the predicted voxels and input in detecting exact instance-specific shapes and poses. Also, for the detection of smaller subjects, a 3D Scale-Aware Residual Dilated Pyramidal Pooling Module (3D-SRDPPM) is adopted that benefits complex scenes. Further, the quad mesh generation from the voxelated grids can be applied for the full 3D mesh for each object. In the training stage, different loss functions are estimated to overcome the losses across each stage in segmenting different levels of burns. Experimental analysis is conducted with various metrics, and their performance is examined with existing techniques. The proposed method achieves improved segmentation results with high accuracy (94.1%), precision (95.9%), recall (94.8%), and F1-score (95.3%).

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  1. Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, Tamil Nadu, 603203, India

    C. Pabitha & B. Vanathi

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  1. C. Pabitha
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Contributions

All the authors have participated in writing the manuscript and have revised the final version. All authors read and approved the final manuscript. All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by PC and VB. The first draft of the manuscript was written by PC, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. PC contributed to conceptualization, methodology, and writing—original draft preparation; PC and VB were involved in formal analysis and investigation; and VB contributed to writing—review and editing and supervision.

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Pabitha, C., Vanathi, B. Dense Mesh RCNN: assessment of human skin burn and burn depth severity. J Supercomput 80, 1331–1362 (2024). https://doi.org/10.1007/s11227-023-05660-y

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