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Skin diseases are among the most common health concerns worldwide, and early diagnosis is crucial to prevent complications and reduce treatment costs. Manual diagnosis requires expert dermatological knowledge and is time-consuming, especially in remote areas with limited access to specialists. This paper proposes an Intelligent Dermatology Assistant, an AI-driven system capable of identifying multiple skin diseases from images, predicting severity, and recommending appropriate medicines and dermatologist information. The system integrates deep learning-based image classification using a CNN model, imputation strategies for handling missing or inconsistent metadata, and a Streamlit-based interactive interface for real-time diagnosis. The model is trained on a curated dataset of skin disease images and achieves improved accuracy through preprocessing, augmentation, and finetuning. The system also provides disease descriptions, causes, deficiencies, and personalized treatment guidelines. Experimental results demonstrate the model’s reliability and suitability for practical deployment as a clinical decision support tool
Keywords:
Dermatology, Deep Learning, Skin Disease Classification, CNN, Medicine Recommendation, Data Imputation, Streamlit, AI in Healthcare.
Cite Article:
" Intelligent Dermatolgy Assistant -AI based Diagnosis and Medicine Recommendation ", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 12, page no.a340-a344, December-2025, Available :http://www.ijrti.org/papers/IJRTI2512041.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator