Intelligent System for Skin Disease Prediction

T. Sriram; M.R.F. Wafra; T.J.N. Fernando; M.A.R.P. Mohottiarachchi

T. Sriram Student
M.R.F. Wafra
T.J.N. Fernando
M.A.R.P. Mohottiarachchi

Keywords: Skin diseases, Image Processing, Machine Learning, Convolutional Neural Network, Natural Language Processing, Treatment Recommendation, Feedback Analysis

Abstract

Being one of our body's primary organs, skin diseases have a significant detrimental impact on our physical and mental well-being and are a growing global health concern. Skin diseases present significant challenges to global health, necessitating timely diagnosis and treatment to mitigate their impact. Leveraging modern technologies such as Artificial Intelligence (Al), Machine Learning (ML), and image processing, Intelligent System for Skin Disease Prediction (ISSDP) have emerged as promising tools to revolutionize dermatological care. This paper provides a succinct overview of the current landscape of skin disease detection and diagnosis, focusing on the development and implementation of ISSDP. By integrating advanced computational techniques including deep learning (DL) architectures, support vector machines (SVM), Convolutional Neural Networks (CNN), and Natural Language Processing (NLP), ISSDP enhances the precision, effectiveness, and accessibility of dermatological care. Key components of ISSDP include image classification, symptom identification, treatment recommendation, and feedback analysis, catering to both the public and healthcare professionals. Through multidisciplinary research integration, this paper elucidates the potential impact of technological advancements in dermatological care. By continuously learning from user feedback and treatment outcomes, ISSDP evolves to refine its diagnostic capabilities over time. In conclusion, ISSDP represents a significant advancement in dermatological care, offering proactive skin health management and promising avenues for improving patient outcomes worldwide.

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