skip to main content
10.1145/3374135.3385327acmconferencesArticle/Chapter ViewAbstractPublication Pagesacm-seConference Proceedingsconference-collections
poster

Utilizing Computer Vision, Clustering and Neural Networks for Melanoma Categorization

Published: 25 May 2020 Publication History

Abstract

With the rise in premiums and deductibles for health insurance, the availability for affordable medical care is nearly out of reach. Because of diminishing coverage, more people opt to visit medical doctors on only extreme need basis. In dermal abnormalities, like Melanoma, there is neglect in treatment due to the high medical costs for consultations and the lack of public knowledge of symptoms. In this study, we propose a software approach to mitigate this problem, which utilizes artificial intelligence with high-level image processing to diagnose and categorize common forms of Melanoma.

References

[1]
[n.d.]. International Skin Imaging Collaboration: Melanoma Project.
[2]
D. Bisla, A. Choromanska, J. Stein, D. Polsky, and R. Berman. 2019. Towards Automated Melanoma Detection with Deep Learning: Data Purification and Augmentation. arXiv:cs.CV/1902.06061
[3]
S. Gilmore, H.P. Soyer, and R. Hofmann-Wellenhof. 2010. A Support Vector Machine for Decision Support in Melanoma Recognition. Experimental Dermatology 19, 9 (Dec 2010), 830--835. https://doi.org/10.1111/j.1600-0625.2010.01112.x
[4]
R. Gogia, M. Binstock, R. Hirose, W.J. Boscardin, M. Chren, and S.T. Arron. 2013. Fitzpatrick Skin Phototype is an Independent Predictor of Squamous Cell Carcinoma Risk After Solid Organ Transplantation. Journal of the American Academy of Dermatology 68, 4 (2013), 585--591. https://doi.org/10.1016/j.jaad.2012.09.030
[5]
A. Namozov and Y. Im Cho. [n.d.]. Convolutional Neural Network Algorithm with Parameterized Activation Function for Melanoma Classification. 2018 International Conference on Information and Communication Technology Convergence (ICTC) ([n. d.]). https://doi.org/10.1109/ictc.2018.8539451
[6]
I. Savoye, C.M. Olsen, D.C. Whiteman, A. Bijon, L. Wald, L. Dartois, F. Clavel-Chapelon, M. Boutron-Ruault, and M. Kvaskoff. 2018. Patterns of Ultraviolet Radiation Exposure and Skin Cancer Risk: the E3N-SunExp Study. Journal of Epidemiology 28, 1 (Jan 2018), 27--33. https://doi.org/10.2188/jea.je20160166
[7]
M. Sudha and B. Poorva. 2019. Predictive Tool for Dermatology Disease Diagnosis using Machine Learning Techniques. International Journal of Innovative Technology and Exploring Engineering Regular Issue 8, 9 (Oct 2019), 355--360. https://doi.org/10.35940/ijitee.g5376.078919
[8]
M.T.B. Toossi, H.R. Pourreza, H. Zare, M.H. Sigari, P. Layegh, and A. Azimi. 2013. An Effective Hair Removal Algorithm for Dermoscopy Images. Skin Research and Technology 19, 3 (Jul 2013), 230--235. https://doi.org/10.1111/srt.12015

Index Terms

  1. Utilizing Computer Vision, Clustering and Neural Networks for Melanoma Categorization

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      ACMSE '20: Proceedings of the 2020 ACM Southeast Conference
      April 2020
      337 pages
      ISBN:9781450371056
      DOI:10.1145/3374135
      Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 25 May 2020

      Check for updates

      Author Tags

      1. Clustering
      2. Convolution Neural Network
      3. Melanoma
      4. Regression
      5. Support Vector Machine

      Qualifiers

      • Poster
      • Research
      • Refereed limited

      Conference

      ACM SE '20
      Sponsor:
      ACM SE '20: 2020 ACM Southeast Conference
      April 2 - 4, 2020
      FL, Tampa, USA

      Acceptance Rates

      Overall Acceptance Rate 502 of 1,023 submissions, 49%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • 0
        Total Citations
      • 63
        Total Downloads
      • Downloads (Last 12 months)1
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 15 Jan 2025

      Other Metrics

      Citations

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media