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Reseach Article

A Simulated Proposed Model for Black Fungus Detection by using Fuzzy Logic

by M.A. El-dosuky, Gamal H. Eladl
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 184 - Number 1
Year of Publication: 2022
Authors: M.A. El-dosuky, Gamal H. Eladl
10.5120/ijca2022921986

M.A. El-dosuky, Gamal H. Eladl . A Simulated Proposed Model for Black Fungus Detection by using Fuzzy Logic. International Journal of Computer Applications. 184, 1 ( Mar 2022), 1-5. DOI=10.5120/ijca2022921986

@article{ 10.5120/ijca2022921986,
author = { M.A. El-dosuky, Gamal H. Eladl },
title = { A Simulated Proposed Model for Black Fungus Detection by using Fuzzy Logic },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2022 },
volume = { 184 },
number = { 1 },
month = { Mar },
year = { 2022 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume184/number1/32294-2022921986/ },
doi = { 10.5120/ijca2022921986 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:20:15.951985+05:30
%A M.A. El-dosuky
%A Gamal H. Eladl
%T A Simulated Proposed Model for Black Fungus Detection by using Fuzzy Logic
%J International Journal of Computer Applications
%@ 0975-8887
%V 184
%N 1
%P 1-5
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recently, some diseases and health problems have appeared that threaten human health. There is a global interest for knowing and preventing spread of pandemic diseases. At the top of importance after COVID-19 is now its different consequences such as Black Fungus that represents an unknown disease for the common. Black Fungus related to people who has low immunity. This research presents an overview of its characteristics, symptoms, and shape that will be manifested in the patient’s body. Unfortunately, the shape of Black Fungus in body can be observed interiorly or exteriorly. The aim and scope of this research is to decompose the complexity of Black Fungus detection into five phases that covers all the forms of Black Fungus. Furthermore, a simulated fuzzy model of Black Fungus detection is presented, with the dependency on a proposed facial disease detection model. This model can be used to prove the relationship between post-Corona and Black Fungus patients. The proposed model can differentiate between the measure of Black Fungus infection risk and COVID-19 in an accurate way. Finally, this research helps in defeating the spread of side effects that may appear.

References
  1. Abel Sb. Quality Of Service-Based Resource Allocation For Web Content Delivery On Cloud Computing Infrastructure. Journal of Theoretical and Applied Information Technology. 2019 Nov 15;97(21).
  2. Jeong W, Keighley C, Wolfe R, Lee WL, Slavin MA, Kong DC, Chen SA. The epidemiology and clinical manifestations of mucormycosis: a systematic review and meta-analysis of case reports. Clinical Microbiology and Infection. 2019 Jan 1;25(1):26-34.
  3. Shevade DS. Mucormycosis: Black Fungus, A Deadly Post- COVID Infection. Microbiology. 2021;2:1.
  4. Skiada A, Lass-Floerl C, Klimko N, Ibrahim A, Roilides E, Petrikkos G. Challenges in the diagnosis and treatment of mucormycosis. Medical mycology. 2018 Apr 1;56(suppl 1):S93- 101
  5. Dannaoui E, Millon L. Current status of diagnosis of mucormycosis: Update on molecular methods. Current Fungal Infection Reports. 2014 Dec 1;8(4):353-9.
  6. Chander J. Textbook of medical mycology. JP Medical Ltd; 2017 Nov 30
  7. Richardson M. The ecology of the Zygomycetes and its impact on environmental exposure. Clinical Microbiology and Infection. 2009 Oct;15:2-9
  8. Petrikkos G, Skiada A, Lortholary O, Roilides E, Walsh TJ, Kontoyiannis DP. Epidemiology and clinical manifestations of mucormycosis. Clinical Infectious Diseases. 2012 Feb 1;54(suppl 1):S23-34
  9. https://github.com/Shivani9707/Black_ fungus-data
  10. Zhang X, Gonnot T, Saniie J. Real-time face detection and recognition in complex background. Journal of Signal and Information Processing. 2017 May 5;8(2):99-112
  11. Viola P, Jones M. Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001 2001 Dec 8 (Vol. 1, pp. I-I). IEEE
  12. Zafeiriou S, Zhang C, Zhang Z. A survey on face detection in the wild: past, present and future. Computer Vision and Image Understanding. 2015 Sep 1;138:1-24
  13. Zhao, Q., Rosenbaum, K., Sze, R., Zand, D., Summar, M. and Linguraru, M.G., 2013, February. Down syndrome detection from facial photographs using machine learning techniques. In Medical Imaging 2013: Computer-Aided Diagnosis (Vol. 8670, p. 867003). International Society for Optics and Photonics.
  14. Zhao Q, Okada K, Rosenbaum K, Kehoe L, Zand DJ, Sze R, Summar M, Linguraru MG. Digital facial dysmorphology for genetic screening: Hierarchical constrained local model using ICA. Medical image analysis. 2014 Jul 1;18(5):699-710
  15. Schneider HJ, Kosilek RP, G¨unther M, Roemmler J, Stalla GK, Sievers C, Reincke M, Schopohl J,W¨urtz RP. A novel approach to the detection of acromegaly: accuracy of diagnosis by automatic face classification. The Journal of Clinical Endocrinology and Metabolism. 2011 Jul 1;96(7):2074-80.
  16. Kong X, Gong S, Su L, Howard N, Kong Y. Automatic detection of acromegaly from facial photographs using machine learning methods. EBioMedicine. 2018 Jan 1;27:94-102
  17. Shu T, Zhang B, Tang YY. An extensive analysis of various texture feature extractors to detect Diabetes Mellitus using facial specific regions. Computers in biology and medicine. 2017 Apr 1;83:69-83.
  18. Hadj-Rabia S, Schneider H, Navarro E, Klein O, Kirby N, Huttner K, Wolf L, Orin M, Wohlfart S, Bodemer C, Grange DK. Automatic recognition of the XLHED phenotype from facial images. American Journal of Medical Genetics Part A. 2017 Sep;173(9):2408-14
  19. Boehringer S, Vollmar T, Tasse C, Wurtz RP, Gillessen- Kaesbach G, Horsthemke B, Wieczorek D. Syndrome identification based on 2D analysis software. European Journal of Human Genetics. 2006 Oct;14(10):1082-9
  20. Gurovich Y, Hanani Y, Bar O, Nadav G, Fleischer N, Gelbman D, Basel-Salmon L, Krawitz PM, Kamphausen SB, Zenker M, Bird LM. Identifying facial phenotypes of genetic disorders using deep learning. Nature medicine. 2019 Jan;25(1):60-4
  21. Li Y, Cha S. Face recognition system. arXiv preprint arXiv:1901.02452. 2019 Jan 8
  22. Wang K, Luo J. Detecting visually observable disease symptoms from faces. EURASIP Journal on Bioinformatics and Systems Biology. 2016 Dec;2016(1):1-8
  23. Jin B, Cruz L, Goncalves N. Deep Facial Diagnosis: Deep Transfer Learning From Face Recognition to Facial Diagnosis. IEEE Access. 2020 Jun 29;8:123649-61
  24. Zadeh, Lotfi A. ”Fuzzy logic= computing with words.” Computing with Words in Information/Intelligent Systems 1. Physica, Heidelberg, 1999. 3-23
  25. Irfan M, Zulfikar WB. Implementation of Fuzzy C-Means algorithm and TF-IDF on English journal summary. In2017 Second International Conference on Informatics and Computing (ICIC) 2017 Nov 1 (pp. 1-5). IEEE
  26. Awotunde JB, Matiluko OE, Fatai OW. Medical diagnosis system using fuzzy logic. African Journal of Computing and ICT. 2014;7(2):99-106
  27. Dagar P, Jatain A, Gaur D. Medical diagnosis system using fuzzy logic toolbox. In International Conference on Computing, Communication and Automation 2015 May 15 (pp. 193- 197). IEEE
Index Terms

Computer Science
Information Sciences

Keywords

Mucormycosis Black Fungus COVID-19 Fuzzy Logic