Review Paper on Implementation on Skin Disease Detection Model using Machine Learning Technique

Authors

  • Pragati V. Zapate Department of Computer Sci. & Engineering Bapurao Deshmukh College of Engineering Sevagram, Wardha
  • Prof. A. D. Gotmare Department of Computer Sci. & Engineering Bapurao Deshmukh College of Engineering Sevagram, Wardha

Keywords:

Image processing technique, skin disease, dermatology

Abstract

Skin diseases are more common than other diseases. Skin Diseases may be caused by fungal infection, bacteria, allergy or viruses etc. The advancement of lasers and Photonics based medical technology has made it possible to diagnose the skin diseases much more quickly and accurately. But the cost of such diagnosis is still limited and very expensive. So, image processing techniques help to build automated screening system for dermatology at an initial stage. The extraction of features plays a key role in helping to classify skin diseases. Computer vision has a role in the detection of skin disease in a variety of techniques.

References

Sergey Damiano, Rajab Chakravarty, Mani Abedin, Classification of dermo copy patterns using deep convo- fluctional neural networks. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pages 364–368. IEEE, 2016.

Harald Gangster, P Pins, Reinhard Rohrer, Ernst Wildling, Michael Binder, and Harald Kettler. Automated melanoma recognition. IEEE transactions on medical imaging, 20(3):233– 239, 2001.

Costantino Grana, Rita Cochair, and Stefania Sei denarii. A new algorithm for border description of polarized light surface microscopic images of pigmented skin lesions. IEEE Transactions on Medical Imaging, 22(8):959–964, 2003.

Sigmundur Sigurdsson, Peter Lars Kai Hansen, Jan Larsen,

Monika Giudecca, and Hans-Christian Wulf. Detection

of skin cancer by classification of Raman spectra. IEEE transactions on biomedical engineering, 51(10):1784–1793, 2004.

Peter Aberg, Ingrid Neander, Johan Hansson, Paul Gelada, Ulf Holmgren, and Stag Elmar. Skin cancer identification using multifrequency electrical impedance-a potential screening tool. IEEE transactions on biomedical engineering, 51(12):2097– 2102, 2004.

Alexander Wong, Jacob Sharkansky , and Paul Foregut. Automatic skin lesion segmentation via iterative stochastic region merging. IEEE Transactions on Information Technology in Biomedicine, 15(6):929–936,

Paul Wighton, Tim K Lee, Harvey Lui, David I McLean, and M Stella Atkins. Generalizing common tasks in automated skin lesion diagnosis. IEEE Transactions on Information Technology in Biomedicine, 15(4):622–629, 2011.

M Emre Celebes, Quan Wen, Sea Hwang, Hitoshi Anatomy, and Ge r a l d S c h a e f e r . Lesion border detection

Downloads

Published

18-04-2022

How to Cite

Pragati V. Zapate, & Prof. A. D. Gotmare. (2022). Review Paper on Implementation on Skin Disease Detection Model using Machine Learning Technique. International Journal for Research Publication and Seminar, 13(3), 56–58. Retrieved from https://jrps.shodhsagar.com/index.php/j/article/view/526

Issue

Vol. 13 No. 3 (2022): Volume 13 | Issue 3 | 2022 (NSCIT)

Section

Original Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Re-users must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. This license allows for redistribution, commercial and non-commercial, as long as the original work is properly credited.