Unbridling the Power of Deep Learning Methodologies for Early Diagnosis of Skin Diseases: A Critical Review | [International Journal of Communication Networks and Information Security • 2024]

Author(s):

1. Ritika Sharma: Department of Computer Science and Engineering, MAIT, Maharaja Agrasen University, Baddi, HP, India

2. Sushil Kumar Bansal: Department of Computer Science and Engineering, MAIT, Maharaja Agrasen University, Baddi, HP, India

Abstract:

Skin diseases affect millions of individuals around the globe and it is a serious health problem. Effective management and treatment of skin disorders depend on a prompt and correct diagnosis; however, access to dermatological care is still restricted, especially in underprivileged areas. The study shows the development of various predictive modeling techniques, whichhave completely changed the interpretation of medical images and opened up exciting new possibilities for the automated diagnosis of skin conditions. Deep learning algorithms have demonstrated outstanding efficacy in the analysis of intricate visual data, including photographs related to dermatology. It may also make it possible for people to selfassess skin lesions, enabling prompt referrals and early diagnosis of potential anomalies. This paper underscores the potential influence of deep learning methods such as convolution neural networks (CNN) which have gained high popularity in accomplishing sustainable healthcare objectives while offering a thorough review of their use in the diagnosis of skin conditions. Recurrent neural networks (RNN), and long- short-term memory (LSTM) models are also examined in this paper. The model demonstrates the advancements made in the automated systems that can correctly diagnose a range of skin disorders, such as eczema, dermatitis, psoriasis, and melanoma. The research supports more general goals of guaranteeing universal access to high-quality healthcare and encouraging sustainable growth.

Page(s): 454-470

DOI: DOI not available

Published: Journal: International Journal of Communication Networks and Information Security, Volume: 16, Issue: 5, Year: 2024

Keywords:

Classification , machine learning , CNN , deep learning , Skin diseases , Diseases Detection

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