Clinical Assessment of Diabetic Foot Ulcers Using GWO-CNN based Hyperspectral Image Processing Approach

REFERENCES

• K. Järbrink, G. Ni, H. Sönnergren, A. Schmidtchen, C. Pang, R. Bajpai, and J. Car, “Prevalence and incidence of chronic wounds and related complications: a protocol for a systematic review,” Syst. Rev., Vol. 5, no. 1, pp. 1–6, 2016.
   PubMedGoogle Scholar
• S. C. Mishra, K. C. Chhatbar, A. Kashikar, and A. Mehndiratta, “Diabetic foot,” Br. Med. J., Vol. 359, pp. 1–7, 2017.
   Google Scholar
• K. Guttormsen, and P. Chadwic, “Diabetic neuropathy: beyond the basics,” J. Diabetes Nur., Vol. 21, no. 1, pp. 17–22, 2017.
   Google Scholar
• B. Zantour, S. Bouchareb, Z. El Ati, F. Boubaker, W. Alaya, W. Kossomtini, and M. H. Sfar, “Risk assessment for foot ulcers among Tunisian subjects with diabetes: a cross sectional outpatient study,” BMC Endocr. Disord., Vol. 20, no. 1, pp. 1–8, 2020.
   PubMed Web of Science ®Google Scholar
• S. Tohme, R. L. Simmons, and A. Tsung, “Surgery for cancer: a trigger for metastases,” Cancer Res., Vol. 77, no. 7, pp. 1548–1552, 2017.
   PubMed Web of Science ®Google Scholar
• G. Lu, “Quantitative detection and delineation of head and neck cancer using hyperspectral imaging and machine learning,” Ph.D. dissertation, Georgia Institute of Technology, 2016.
   Google Scholar
• W. Lv, and X. Wang, “Overview of hyperspectral image classification,” J. Sens., Vol. 2020, pp. 1–13, 2020.
   Web of Science ®Google Scholar
• R. Hepzibai, T. Arumuga Maria Devi, P. Darwin, and E. SenthilKumar, “Detection of normal and abnormalities from diabetics patient’s foot on hyperspectral image processing,” in Innovations in computer science and engineering, Singapore: Springer, 2020, pp. 57–64.
   Google Scholar
• T. A. M. Devi, and P. T. Selvi, “Feature extraction and classification of blood cell on medical hyperspectral imagery for multi-graph convolution neural network,” Solid State Technol., Vol. 63, no. 6, pp. 4234–4244, 2020.
   Google Scholar
• D. T. A. M. Devi, and G. A. Arumugaraj. “Novel technique of resolution enhancement in hyperspectral images on proposed CHLAE technique.” In International Conference on Soft Computing in Applied Sciences and Engineering organized by the Department of Electronics and Instrumentation Engineering, Conference proceedings, 2015.
   Google Scholar
• T. A. M. Devi, and I. Rajeswari, “Hyperspectral band clustering on ebcot pre encoding technique,” J. Chem. Pharm. Sci., Vol. 9, no. 1, pp. 6359–6366, 2016.
   Google Scholar
• P. Darwin, and T. A. M. Devi. “Quality assurance of chicken meat on hyperspectral image processing,” In 1ST international conference on computer communications & power systems (ICCCPS), 2020, pp. 334–339.
   Google Scholar
• R. Johnson, A. Osbourne, J. Rispoli, and C. Verdin, “The diabetic foot assessment,” Orthop. Nurs., Vol. 37, no. 1, pp. 13–21, 2018.
   PubMed Web of Science ®Google Scholar
• T. A. Maria, and D. M. P. Darwin, “Deep learning approach for food quality inspection and improvement on hyper spectral fruit images,” Ann. Roman. Soc. Cell Biol., Vol. 25, pp. 15682–15696, 2021.
   Google Scholar
• T. A. M. Devi, and S. Percy, “An efficiently identify the diabetic foot ulcer based on foot anthropometry using hyperspectral imaging,” Int. J. Inf. Technol. Manag. Inf. Syst., Vol. 7, no. 2, pp. 36–44, 2016.
   Google Scholar
• R. Ghotaslou, M. Y. Memar, and N. Alizadeh, “Classification, microbiology and treatment of diabetic foot infections,” J. Wound Care, Vol. 27, no. 7, pp. 434–441, 2018.
   PubMed Web of Science ®Google Scholar
• M. Monteiro-Soares, E. J. Boyko, W. Jeffcoate, J. L. Mills, D. Russell, S. Morbach, and F. Game, “Diabetic foot ulcer classifications: a critical review,” Diabetes Metab. Res. Rev., Vol. 36, pp. e3272, 2020.
   PubMed Web of Science ®Google Scholar
• R. R. Yotsu, N. M. Pham, M. Oe, T. Nagase, H. Sanada, H. Hara, and T. Tamaki, “Comparison of characteristics and healing course of diabetic foot ulcers by etiological classification: neuropathic, ischemic, and neuro-ischemic type,” J. Diabetes Complicat., Vol. 28, no. 4, pp. 528–535, 2014.
   PubMed Web of Science ®Google Scholar
• Y. Huang, T. Xie, Y. Cao, M. Wu, L. Yu, S. Lu, and H. Ruan, “Comparison of two classification systems in predicting the outcome of diabetic foot ulcers: The Wagner grade and the Saint Elian Wound score systems,” Wound Repair. Regen., Vol. 23, no. 3, pp. 379–385, 2015.
   PubMed Web of Science ®Google Scholar
• A. Bravo-Molina, J. P. Linares-Palomino, B. Vera-Arroyo, L. M. Salmerón-Febres, and E. Ros-Díe, “Inter-observer agreement of the Wagner. University of Texas and PEDIS classification systems for the diabetic foot syndrome,” Foot Ankle Surg., Vol. 24, no. 1, pp. 60–64, 2018.
   PubMed Web of Science ®Google Scholar
• C. E. Fife, S. D. Horn, R. J. Smout, R. S. Barrett, and B. Thomson, “A predictive model for diabetic foot ulcer outcome: The wound healing index,” Adv. Wound Care, Vol. 5, no. 7, pp. 279–287, 2016.
   Web of Science ®Google Scholar
• F. Game, “Classification of diabetic foot ulcers,” Diabetes Metab. Res. Rev., Vol. 32, pp. 18–194, 2016.
   Web of Science ®Google Scholar
• F. Chuan, K. Tang, P. Jiang, B. Zhou, and X. He, “Reliability and validity of the perfusion, extent, depth, infection and sensation (PEDIS) classification system and score in patients with diabetic foot ulcer,” PLoS One, Vol. 10, no. 4, pp. e0124739, 2015.
   PubMed Web of Science ®Google Scholar
• C. Liu, J. J. van Netten, J. G. Van Baal, S. A. Bus, and F. van Der Heijden, “Automatic detection of diabetic foot complications with infrared thermography by asymmetric analysis,” J. Biomed. Opt., Vol. 20, no. 2, pp. 026003, 2015.
   PubMed Web of Science ®Google Scholar
• L. Fraiwan, M. AlKhodari, J. Ninan, B. Mustafa, A. Saleh, and M. Ghazal, “Diabetic foot ulcer mobile detection system using smart phone thermal camera: A feasibility study,” Biomed. Eng. Online, Vol. 16, no. 1, pp. 1–19, 2017.
   PubMedGoogle Scholar
• P. Nagabushanam, S. Thomas George, and S. Radha, “EEG signal classification using LSTM and improved neural network algorithms,” Soft Comput., Vol. 24, no. 13, pp. 9981–10003, 2020.
   Web of Science ®Google Scholar
• M. K. Gandhimathi, “DP-RNN: type II diabetic prediction using GKFCM and RNN,” Turk. J. Comp. Math. Educ., Vol. 12, no. 10, pp. 6093–6105, 2021.
   Google Scholar
• B. Cassidy, N. D. Reeves, P. Joseph, D. Gillespie, C. O'Shea, S. Rajbhandari, and M. H. Yap, “Dfuc2020: analysis towards diabetic foot ulcer detection,” arXiv preprint arXiv, Vol. 2004, pp. 11853, 2020.
   Google Scholar
• M. Goyal, N. D. Reeves, S. Rajbhandari, and M. H. Yap, “Robust methods for real-time diabetic foot ulcer detection and localization on mobile devices,” IEEE J. Biomed. Health. Inform., Vol. 23, no. 4, pp. 1730–1741, 2018.
   PubMed Web of Science ®Google Scholar
• M. Goyal, N. D. Reeves, A. K. Davison, S. Rajbhandari, J. Spragg, and M. H. Yap, “Dfunet: Convolutional neural networks for diabetic foot ulcer classification,” IEEE Trans. Emerg. Topics Comp. Intell., Vol. 4, no. 5, pp. 728–739, 2018.
   Google Scholar
• L. Alzubaidi, M. A. Fadhel, S. R. Oleiwi, O. Al-Shamma, and J. Zhang, “DFU_QUTNet: Diabetic foot ulcer classification using novel deep convolutional neural network,” Multimed. Tools Appl., Vol. 79, no. 21, pp. 15655–15677, 2020.
   Web of Science ®Google Scholar
• F. J. Veredas, R. M. Luque-Baena, F. J. Martín-Santos, J. C. Morilla-Herrera, and L. Morente, “Wound image evaluation with machine learning,” Neurocomputing, Vol. 164, pp. 112–122, 2015.
   Web of Science ®Google Scholar
• S. S. Devi, V. K. Solanki, and R. H. Laskar, “Recent advances on big data analysis for malaria prediction and various diagnosis methodologies,” Handbook of Data Science Approaches for Biomedical Engineering, 153–184, 2020.
   Google Scholar
• M. Esfahanian, H. Zhuang, and N. Erdol, “Using local binary patterns as features for classification of dolphin calls,” J. Acoust. Soc. Am., Vol. 134, no. 1, pp. EL105–EL111, 2013.
   PubMed Web of Science ®Google Scholar
• T. A. M. Devi, “Habitual detection and measurement of human blood cells on hyperspectral imagery for convolutional neural network,” Ann. Roman. Soc. Cell Biol., vol. 25, pp. 15670–15681, 2021.
   Google Scholar