Survey on Pores and Skin Disease Classification using Deep Neural Community

Authors

  • C. Mohammed Gulzar Research Scholar, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamilnadu, India
  • S. Meera Associate Professor, Vels Institute of Science, Technology & Advanced Studies (VISTAS), Chennai, Tamilnadu, India

Keywords:

CNN, face skin illness, classification, deep learning

Abstract

Skin disease diagnosis and prognosis have historically been challenging and crucial tasks for medical professionals.  The majority of pore and skin care professionals in the modern world still use antiquated methods for diagnosing conditions, which may be quite time-consuming. Many machine learning models, which are sophisticated and take longer to analyse, are utilised in the methods now in use. Therefore, in order to understand the differences in performance between various models, a variety of deep learning models are examined in this work. Skin diseases are a major concern these days since they may result from a variety of environmental variables, socioeconomic problems, losing track of a weight reduction programme, and other things. This essay compares and contrasts skin diseases associated with common skin conditions and cosmetics. The crucial processes for oily, dry, and regular pores include image selection, segmentation, and classification in the detection and classification of skin diseases. Based on the technologies utilised, accurate results, moral behaviour, number of diseases detected, and datasets, a survey of many studies is obtained. To explain the better performance of deep learning models, current deep learning architectures are compared with various research approaches.

Downloads

Download data is not yet available.

References

Z. Wu et al is developed by Studies on different CNN algorithms for face skin disease classification based on clinical images, doi: 10.1109/ACCESS.2019.2918221.

E. Groceri is developed by analysis of deep networks with residual blocks and different activation functions: classification of skin diseases, doi: 10.1109/IPTA.2019.8936083

K. Melbin and Y.J.V are developed by Raj, An enhanced model for skin disease detection using dragonfly optimization based deep neural network doi: 10.1109/I-SMAC47947.2019.9032458

M. Akay et al. is developed by Deep learning classification of systemic sclerosis skin using the MobileNetV2 Model, doi: 10.1109/OJEMB.2021.3066097.

Zhao J, Lui H are developed by Real time Raman Spectroscopy for non-invasive skin cancer detection preliminary results doi: 10.1109/IEMBS.2008.4649861

M.N. Hosen, V. Panneerselvam, are developed by, Federated machine learning for detection of skin disease and enhancement of internet of medical things, doi: 10.1109/JBHI.2022.3149288.

Hay RJ, Johns NE, Williams HC, are developed by The global burden of skin disease in 2010: an analysis of the prevalence and impact of skin conditions.. doi: 10.1038/jid.2013.446

R. Karthik, Tejas sunil vaichole, are developed by, Eff2Net: An efficient channel attention-based convolutional neural network for skin disease classification, https://doi.org/10.1016/j.bspc.2021.103406.

B. Ahmad, M. Usama, are developed by "Discriminative Feature Learning for Skin Disease Classification Using Deep Convolutional Neural Network, doi: 10.1109/ACCESS.2020.2975198.

Abuzaghleh, Omar, Buket D. Barkana, are developed by "Noninvasive real-time automated skin lesion analysis system for melanoma early detection and prevention.

A. Bewley, is developed by ‘‘The neglected psychological aspects of skin disease,’’ Brit. Med. J., vol. 358, p. 3208, Jul. 2017.

Maglogiannis, Ilias, Elias Zafiropoulos, are developed by "Intelligent segmentation and classification of pigmented skin lesions in dermatological images."

Lakshay Bajaj, et al., is developed by “Automated system for prediction of skin disease using image processing and machine learning ”

D.Khanna et al, is developed by “Standardization of the modified Rodanan Skin score for use in clinical trials of systemic sclerosis”

P. Clements et al. is developed by “Inter and intraoberver variability of total skin thickness score in systemic sclerosis”

Downloads

Published

24.03.2024

How to Cite

Gulzar, C. M. ., & Meera, S. . (2024). Survey on Pores and Skin Disease Classification using Deep Neural Community . International Journal of Intelligent Systems and Applications in Engineering, 12(19s), 73–77. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/5045

Issue

Vol. 12 No. 19s (2024)

Section

Research Article

License

Creative Commons License

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

All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.

IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.