Machine Learning-Based Detection System for Facial Skin Diseases and Ayurvedic Remedies

Authors : R.K.A. Risina Rasmith; C.P. Abeywickrama; H.L.D.P. De Silva; K.G. Chamindu Hansana; S. Siriwardana; S. Jayaweera

Volume/Issue : Volume 8 - 2023, Issue 10 - October

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Facial skincare is crucial for overall health, beauty, and wellbeing, as the face serves as a foremost reflection of one's life. Face skin is delicate and more sensitive to damage. Neglecting proper facial skincare can lead to some diseases like acne, dark spots, and other signs of aging. Facial skin diseases are a common health problem that can be found worldwide. Since ancient times, ayurvedic treatments can be found as natural and optimum solutions to cure those diseases and keep the facial skin healthy and glowing. This research introduces an integrated framework to automate face skin disease detection, identify ayurvedic plants, patient data management and telemedicine system, and user interaction within the context of facial skin health. The proposed system utilizes machine learning techniques to identify four facial skin conditions: acne, dark circles, dark spots, and wrinkles into 3 levels. Additionally, it can recognize 20 different ayurvedic plants using leaves, flowers, fruits, and barks and offers ayurvedic remedies through natural language processing (NLP) based emotional awareness chatbot using text and audio messages and enhances patient engagement through a web application using telemedicine system, connecting medical professionals and patients for efficient care delivery. This research aims to reform skincare by combining advanced technology with traditional knowledge, offering holistic solutions to facial skin health. It addresses the need for early face skin disease detection, natural remedies, and seamless patient-professional interaction, eventually promoting a healthy and fair appearance.

Keywords : Facial Skincare, Disease Detection, Ayurvedic Plants, Telemedicine, Natural Remedies.


Paper Submission Last Date
31 - December - 2023

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