• 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 Deep Learning Algorithms for an Early Detection and Diagnosis of Skin Disease

Sakshi Loura

Bharti International School, Rewari

64 - 67 Vol. 7, Jan-Dec, 2021
Receiving Date: 2021-02-28;    Acceptance Date: 2021-04-20;    Publication Date: 2021-05-03
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Abstract

Melanoma and Nevus (mole) cancer growth can be dangerous skin infections because the development pace of this kind of cancer growth is very high, requiring an assessment for the right on-time determination to dermatological consideration for the overall population. Beyond that, 95% of skin malignant growths can be effectively treated if they are analyzed early [1]. It is costly for various people in most farming nations. AI and deep learning procedures are significant in picture reports, design acknowledgment, and surface examination. These can create AI and deep learning procedures in cell phones to contact everyone. In this study, we break down different kinds of AI and deep learning procedures for diagnosing skin diseases in patients.

    References

  1. https://www.betterhealth.vic.gov.au/health/conditionsandtreatments/melanoma
  2. https://www.wcrf.org/dietandcancer/skin-cancer
  3. https://www.medicinenet.com/skin_cancer_overview/article.htm
  4. Philipp Tschandl et al,. “The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions”, (2018). https://arxiv.org/abs/1803.10417
  5. K. Polat and K. O. Koc, “Detection of Skin Diseases from Dermoscopy Image Using the combination of Convolutional Neural Network and One-versus-All”, Journal of Artificial Intelligence and Systems, 2020, 2, 80-97.
  6. T.C. Pham et al.,” Improving Skin-Disease Classification Based on Customized Loss Function”, IEEE Access, August 26, 2020.
  7. Kushagra Mahajan, Monika Sharma, Lovekesh Vig, “Meta-DermDiagnosis: Few-Shot Skin Disease Identification using Meta-Learning”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 730-731
  8. Kushagra Mahajan, Monika Sharma, Lovekesh Vig, “Meta-DermDiagnosis: Few-Shot Skin Disease Identification using Meta-Learning”, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2020, pp. 730-731
  9. J. Kawahara et al., “Seven-point checklist and skin lesion classification using multitask multimodal neural net,” IEEE journal of biomedical and health informatics, 23(2):538–546, 2018.
  10. J. Kawahara et al., “Seven-point checklist and skin lesion classification using multitask multimodal neural net,” IEEE journal of biomedical and health informatics, 23(2):538–546, 2018.
  11. T. Majtner et al., “Ensembling Convolutional Neural Networks for Skin Cancer Classification,” (2018), https://arxiv.org/abs/1808.05071.
  12. Nils Gessert et al., “ Skin Lesion Diagnosis using Ensembles, Unscaled Multi-Crop Evaluation and Loss Weighting” (2018).
  13. Philipp Tschandl, Christoph Sinz, Harald Kittler, “Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation”, Computers in Biology and Medicine, 104, 2019, 111-116.
  14. Philipp Tschandl, Christoph Sinz, Harald Kittler, “Domain-specific classification-pretrained fully convolutional network encoders for skin lesion segmentation”, Computers in Biology and Medicine, 104, 2019, 111-116.
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