Design and Analysis of CNN-Based Skin Disease Detection System with Preliminary Diagnosis

DOI

10.2991/978-94-6463-252-1_37How to use a DOI?

Keywords

CNN architecture; Python; Skin disease; Deep learning; Image processing; Naturopathy

Abstract

Over the past few decades, the occurrence of skin diseases has increased, putting a significant strain on healthcare systems worldwide. These skin diseases can be cancerous (e.g., basal and squamous cell carcinoma, melanoma) and non- cancerous (e.g., acne, vitiligo and eczema). Skin problems can be detrimental to physical health and can cause psychological problems, usually in patients whose face is disfigured or damaged due to skin problems. These dermatologic disorders worsen the situation as time progresses, but the survival rates are high if detected and diagnosed early. This article provides a comprehensive overview of the methods used to classify and detect skin disorders as well as diagnostic methods using naturopathic methods. This paper likewise briefs about the openly available image pre-processing mechanisms and classification algorithms based on the relevant works performed by researchers across the world, and suggests the most suitable technique for each process involved in the skin disease system with appropriate results.

Copyright

© 2023 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - T. Vasudeva Reddy
AU  - R. Anirudh Reddy
AU  - K. Sai Prasanna
AU  - C. S. Bhanu Teja
AU  - N. Sai Chara n Reddy
AU  - N. Hima Chandra Sekhar Rao
PY  - 2023
DA  - 2023/11/09
TI  - Design and Analysis of CNN-Based Skin Disease Detection System with Preliminary Diagnosis
BT  - Proceedings of the Second International Conference on Emerging Trends in Engineering (ICETE 2023)
PB  - Atlantis Press
SP  - 334
EP  - 346
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-252-1_37
DO  - 10.2991/978-94-6463-252-1_37
ID  - Reddy2023
ER  -