• E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2020

    5.051

    Impact Factor 2021

    5.610

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2020

    5.051

    Impact Factor 2021

    5.610

  • E-ISSN:

    2454-9584

    P-ISSN

    2454-8111

    Impact Factor 2020

    5.051

    Impact Factor 2021

    5.610

INTERNATIONAL JOURNAL OF INVENTIONS IN ENGINEERING & SCIENCE TECHNOLOGY

International Peer Reviewed (Refereed), Open Access Research Journal
(By Aryavart International University, India)

Paper Details

Leveraging the Convoluted Neural Network(CNN) for Enhancing the Efficacy of the Early Diagnosis of Disease using Thumbnail Images

Updesh Sachdeva

Mount Olympus School, Gurugram, Haryana, India

35 - 39 Vol. 8, Jan-Dec, 2022
Receiving Date: 2022-05-10;    Acceptance Date: 2022-07-15;    Publication Date: 2022-08-02
Download PDF

Abstract

The system's main goal is to find the disease without harming people. Observing a person's nails can reveal the presence of a number of diseases. Be that as it may, it tends to be extremely challenging for our eyes to track down varieties in the shade of nails. Our framework can conquer the constraint since the entire cycle occurs through the PC. Nail images serve as the system's input. The framework takes the nail picture of the individual and attempts to recognize assuming that any highlights are available. The patterns and colors of the nails can help identify diseases. Here, first, the nail pictures are prepared with different illnesses through the CNN model. These prepared pictures of nails are contrasted with the information picture with distinguish the sickness. The disease will be identified if the features of the input nail image and the trained nail images match. The nail images are subjected to various processes in order to accurately identify the features. The necessary features are extracted from the images through accurate analysis and processing.

Keywords: CNN; thumbnail images; diagnosis of disease

    References

  1. Indi Trupti, Yogesh A. Gunge, “Early Stage Disease Diagnosis System Using Human Nail Image Processing”, I.J. Information Technology And Computer Science, Vol. 7, Pp.30-35 , 05-07-2016.
  2. V.Saranya,A.Ranichitra,”Image Segmentation Techniques To Detect Nail Abnormalities”, International Journal Of Computer Technology & Applications, Vol.-8, Issue- 4, Pp.522-527, 2017.
  3. Sujatha R, Y Sravan Kumar, Garine Uma Akhil, “Leaf Disease Detection Using Image Processing”, Journal Of Chemical and Pharmaceutical Sciences, Volume 10 Issue 1, Pp.670-672, January- March 2017.
  4. Sneha Gandhat, A. D. Thakare, Swati Avhad, Nityash Bajpai, Rohit Alawadhi, “ Study And Analysis Of Nail Images Of Patients”, International Journal Of Computer Applications, Volume 143 No.13, June 2016
  5. Indrakumar S S, Dr. M S Shashidhara, ”Study On Eye Troubles Using Palm Print And Image Processing Technique”, International Journal Of Recent Trends In Engineering & Research (Ijrter), Volume 02, Issue 05, Pp. 198-201, May –2016.
  6. Sima Kumari, Neelamegam P, Abirami S, ”Analysis Of Apple Fruit Diseases Using Neural Network”, Research Journal Of Pharmaceutical, Biological And Chemical Sciences, Vol.6, Issue 4, Pp.641-646, July– August 2015.
  7. Hardik Pandit, Dr. D M Shah,” A System For Nail Color Analysis In Healthcare”, International Conference On Intelligent Systems And Signal Processing, Pp. 221-223, 2013.
  8. Vipra Sharma, Aparajit Shrivastava,” System For Disease Detection By Analyzing Fingernails Color And Texture”, International Journal Of Advanced Engineering Research And Science (Ijaers), Vol-2, Issue-10, Oct- 2015.
  9. Kumuda N S, Dinesh M S, “Human Fingernail Segmentation”, International Conference On Emerging Research In Electronics, Computer Science And Technology – 2015, Pp. 270-275, 2015.
  10. Noriaki Fujishima, Kiyoshi Hoshino, “Fingernail Detection Method From Hand Images Including Palm”, Iapr International Conference On Machine Vision Applications, Pp. 117-120, May 20-23, 2013.
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