Enhanced skin burn assessment through transfer learning: a novel framework for human tissue analysis

References

• Dexter G, Patil S, Singh K, et al. Clinical outcomes after burns in elderly patients over 70 years: a 17-year retrospective analysis. Burns. 2018;44(1):65–69. doi: 10.1016/j.burns.2017.09.018.
   PubMed Web of Science ®Google Scholar
• Rivas E, Herndon DN, Chapa ML, et al. Children with severe burns display no sex differences in exercise capacity at hospital discharge or adaptation after exercise rehabilitation training. Burns. 2018;44(5):1187–1194.
   PubMed Web of Science ®Google Scholar
• Abubakar A, Ugail H, Bukar AM, et al. Discrimination of healthy skin, superficial epidermal burns, and full-thickness burns from 2D-colored images using machine learning. In: Data science. Boca Raton (FL): CRC Press; 2019. p. 201–223.
   Google Scholar
• Torvi DA, Dale JD. A finite element model of skin subjected to a flash fire. J Biomech Eng. 1994;116(3):250–255. doi: 10.1115/1.2895727.
   PubMed Web of Science ®Google Scholar
• Richardson PD, Whitelaw JH. Transient heat transfer in human skin. J. Franklin Inst. 1968;286(3):169–181. doi: 10.1016/0016-0032(68)90503-6.
   Web of Science ®Google Scholar
• Badea M-S, Vertan C, Florea C, et al. Automatic burn area identification in color images. Proc. Int. Conf. Commun. (COMM); 2016. p. 65–68.
   Google Scholar
• Rangaraju LP, Kunapuli G, Every D, et al. Classification of burn injury using Raman spectroscopy and optical coherence tomography: an ex-vivo study on porcine skin. Burns. 2019;45(3):659–670. doi: 10.1016/j.burns.2018.10.007.
   PubMed Web of Science ®Google Scholar
• Sabeena B, Kumar PDR. Diagnosis and detection of skin burn analysis segmentation in colour skin images. Int J Adv Res Comput Commun Eng. 2017;6(2):369–374.
   Google Scholar
• Suvarna M, Niranjan UC. Classification methods of skin burn images. Int J Comput Sci Inf Technol. 2013;5(1):109–118. doi: 10.5121/ijcsit.2013.5109.
   Google Scholar
• Yadav DP, Sharma A, Singh M, et al. Feature extraction-based machine learning for human burn diagnosis from burn images. IEEE J Transl Eng Health Med. 2019;7:1800507. doi: 10.1109/JTEHM.2019.2923628.
   PubMedGoogle Scholar
• Chauhan J, Goswami R, Goyal P. Using deep learning to classify burnt body parts images for better burns diagnosis. In Processing and Analysis of Biomedical Information: First International SIPAIM Workshop, SaMBa 2018, Held in Conjunction with MICCAI 2018; 2018 Sep 20; Granada, Spain; Revised Selected Papers 1. Springer International Publishing; 2019. p. 25–32.
   Google Scholar
• Abubakar A, Ugail H, Bukar AM. Assessment of human skin burns: a deep transfer learning approach. J Med Biol Eng. 2020;40(3):321–333. doi: 10.1007/s40846-020-00520-z.
   Web of Science ®Google Scholar
• Rehman-Butt AU, Ahmad W, Ashraf R, et al. Computer-aided diagnosis (CAD) for segmentation and classification of burnt human skin. Proceedings of the 2019 International Conference on Electrical, Communication, and Computer Engineering (ICECCE); 2019. p. 5. doi: 10.1109/ICECCE47252.2019.8940758.
   Google Scholar
• Evik U, Karakullukçu E, Berber T, et al. Automatic classification of skin burn color images using texture-based feature extraction. IET Image Process. 2019;13(11):2018–2028. doi: 10.1049/iet-ipr.2018.5899.
   Web of Science ®Google Scholar
• Suvarna M, Toney G, Swastik G. Classification of scalding burn using image processing methods. 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT); 2017. p. 1312–1315. doi: 10.1109/ICICICT1.2017.8342759.
   Google Scholar
• Abubakar A, Ugail H. Discrimination of human skin burns using machine learning. Intelligent Computing-Proceedings of the Computing Conference; 2019. p. 641–647.
   Google Scholar
• Abubakar A, Ugail H, Bukar AM. Can machine learning be used to discriminate between burns and pressure ulcers? Proceedings of SAI Intelligent Systems Conference; 2019. p. 870–880.
   Google Scholar
• Mikołajczyk A. Department of Control Systems Engineering, Gdansk University of Technology, Gdansk, Poland, Michał Grochowski, Department of Control Systems Engineering, Gdansk University of Technology, Gdansk, Poland. Available from: https://ieeexplore.ieee.org/document/8388338
   Google Scholar
• Yamashita R. Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan, Mizuho Nishi, Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japa, Preemptive Medicine and Lifestyle Disease Research Center, Kyoto University Hospital, 53 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan, Richard Kinh Gian Do, Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA, Kaori Togashi, Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, 54 Kawahara-cho, Shogoin, Sakyo-ku, Kyoto, 606-8507, Japan. Available from: https://insightsimaging.springeropen.com/articles/10.10 07/s13244-018-0639-9
   Google Scholar
• Department of Radiology. Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY, 10065, USA, Jia Deng, Department of Computer Science, Princeton University, Wei Dong, Department of Computer Science, Princeton University, USA, Richard Socher, Department of Computer Science, Princeton University, USA, Li-Jia Li, Department of Computer Science, Princeton University, USA, Kai Li, Department of Computer Science, Princeton University, USA, Li Fei-Fei, Department of Computer Science, Princeton University, USA. Available from: https://ieeexplore.ieee.org/document/5206848
   Google Scholar
• Abubakar A, Ugail H. Discrimination of human skin burns using machine learning. Intelligent Computing—Proceedings of the Computing Conference. London: Springer; 2019. p. 641–647.
   Google Scholar