A Review of Deep Learning Approach for Classification and Efficacy Testing of Chhattisgarh Herbal Ingredients in Skincare

DOI

https://www.doi.org/10.59256/indjcst.20260502091

Published In

Volume 5, Issue 2 (2026)

Published Online

May-August 2026

Pages

846-851

Article Type

Review Article

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Skin diseases are among the most common health problems affecting individuals of all age groups worldwide. Early diagnosis and appropriate treatment are essential to prevent severe complications and improve patient outcomes. However, access to dermatological care is often limited due to factors such as cost, geographical barriers, and the shortage of specialists. At the same time, medicinal plants have been widely used in traditional healthcare systems for the treatment of various skin disorders because of their therapeutic and healing properties. The integration of Artificial Intelligence (AI) with traditional herbal knowledge offers a promising approach for enhancing skin disease management and promoting natural treatment alternatives. This study proposes a deep learning-based framework for the classification of skin diseases and the efficacy assessment of selected herbal ingredients commonly found in Chhattisgarh. The proposed system utilizes medical image analysis techniques to identify skin diseases from dermatological images. The methodology involves image acquisition, preprocessing, feature extraction, and disease classification using a Convolutional Neural Network (CNN). Following disease identification, the system recommends suitable herbal ingredients such as neem, tulsi, aloe vera, turmeric, amla, and sandalwood based on their documented medicinal properties and effectiveness against specific skin conditions. The framework aims not only to classify skin diseases accurately but also to evaluate the relevance and potential efficacy of herbal remedies through a knowledge-based recommendation approach. Experimental analysis demonstrates that deep learning models can effectively recognize skin disease patterns and support decision-making for herbal treatment recommendations. The integration of AI-driven diagnosis with traditional medicinal knowledge provides users with preliminary healthcare guidance and encourages the utilization of natural skincare solutions. The proposed research contributes to the fields of artificial intelligence, healthcare informatics, and herbal medicine by presenting an intelligent system that bridges modern technology and traditional therapeutic practices. The approach has potential applications in telemedicine, healthcare support systems, herbal research, and the promotion of indigenous medicinal resources for skincare management.