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Automatic skull prototy** framework for damage detection and repairing using computer vision and deep learning techniques

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Abstract

Bio-Medical modeling system used to assist medical study, diagnosis, analysis, monitoring, and investing in the medical domain. The medical scanning tools scan, collect, and assemble the fragmented skull-specific geometric data before the medical analysis by various experts for investigations. The skull assembly may undergo severe damage, significantly affecting the medical analysis process. Therefore, need to have an efficient and robust automatic skull prototy** technique. This article proposes the novel Automatic Skull Prototy** (ASP) framework using deep learning and computer vision technique. The ASP framework consists of two main phases: Skull Damage Detection (SDD) and Skull Damage Repairing (SDR). For SDD, we propose the integrated deep learning model using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The SDR model performs the template loading and template matching process to discover the damaged regions and repair them by fusing the missing geometric data of the skull template into the original damaged skull. Experimental results demonstrate the improved efficacy and robustness of the proposed framework. The SDR outcomes show the effective repairing of skull models but scalability limitations. The real-time skull model dataset preparation and analysis will be interesting for future research. The ASP framework benefited the forensic, archaeological, anthropological, biomedical applications for processing, analysis, investigation, and diagnosis.

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Acknowledgements

I would like to express my deepest gratitude and thanks to Dr Kiran Bhole, Dr. S. S. Umale and Dr. P. H. Sawant, R & D Dean, Head of Mechanical Engineering Department, and Principal, Sardar Patel college of Engineering, Mumbai for their support, guidance and motivation during the various phases of writing this article. We also extend thanks to Dr. S. S. Mantha, former chairman, AICTE, Delhi, India for his continuous technical support and guidance.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Sardar Patel College of Engineering, Andheri (W), Mumbai, India

    Amol Mangrulkar, Santosh B. Rane & Vivek Sunnapwar

  2. Department of Mechanical Engineering, Lokmanya Tilak College of Engineering, Koparkhairane, Navi Mumbai, India

    Vivek Sunnapwar

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  1. Amol Mangrulkar
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  2. Santosh B. Rane
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Correspondence to Amol Mangrulkar.

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Mangrulkar, A., Rane, S.B. & Sunnapwar, V. Automatic skull prototy** framework for damage detection and repairing using computer vision and deep learning techniques. Int. j. inf. tecnol. 14, 3527–3537 (2022). https://doi.org/10.1007/s41870-022-00956-3

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