Article: Detection of skin issues in face using deep learning: an empirical approach for 21st century Healthcare 5.0 Journal: International Journal of Creative Computing (IJCRC) 2024 Vol.2 No.2 pp.148 - 171 Abstract: Facial skin problems can have a profound impact on an individual's self-esteem and mental well-being, sometimes leading to depression. Early detection and treatment of these conditions are crucial for effective intervention. The aim is to accurately classify facial skin diseases, including acne, actinic keratosis, basal cell carcinoma, eczema, and rosacea. The objectives of the presented research work are that the proposed system offers a potential pre-diagnostic tool, enabling individuals to assess their facial conditions before consulting a dermatologist. Also to offer a seamless solution to check skin diseases on the face, to empower individuals with effortless convenience, one can revolutionise facial health monitoring, putting control at their fingertips. The key idea is to help in regular monitoring of skin health also severe skin conditions can be diagnosed in early stages. The authors have presented an interesting research topic which analyses different well-known algorithms to detect different categories of skin disease at early stage. The paper presents the accuracy of CNN, Deep CNN and random forest models with accuracy up to 56.21%, which is low. The paper is expected to propose a new model with higher accuracy on test data compared to other existing techniques. Inderscience Publishers - linking academia, business and industry through research

Title: Detection of skin issues in face using deep learning: an empirical approach for 21st century Healthcare 5.0

Authors: Rohit Rastogi; Mohd. Shahjahan; Piyush Yadav

Addresses: Department of CSE, ABES Engineering College, Ghaziabad, U.P., India ' Department of CSE, ABES Engineering College, Ghaziabad, U.P., India ' Department of CSE, ABES Engineering College, Ghaziabad, U.P., India

Abstract: Facial skin problems can have a profound impact on an individual's self-esteem and mental well-being, sometimes leading to depression. Early detection and treatment of these conditions are crucial for effective intervention. The aim is to accurately classify facial skin diseases, including acne, actinic keratosis, basal cell carcinoma, eczema, and rosacea. The objectives of the presented research work are that the proposed system offers a potential pre-diagnostic tool, enabling individuals to assess their facial conditions before consulting a dermatologist. Also to offer a seamless solution to check skin diseases on the face, to empower individuals with effortless convenience, one can revolutionise facial health monitoring, putting control at their fingertips. The key idea is to help in regular monitoring of skin health also severe skin conditions can be diagnosed in early stages. The authors have presented an interesting research topic which analyses different well-known algorithms to detect different categories of skin disease at early stage. The paper presents the accuracy of CNN, Deep CNN and random forest models with accuracy up to 56.21%, which is low. The paper is expected to propose a new model with higher accuracy on test data compared to other existing techniques.

Keywords: skin disease classification; facial skin conditions; convolutional neural networks; CNN; random forest; image analysis; feature extraction; diagnosis; treatment planning.

DOI: 10.1504/IJCRC.2024.138235

International Journal of Creative Computing, 2024 Vol.2 No.2, pp.148 - 171

Received: 17 Jul 2023
Accepted: 29 Sep 2023
Published online: 30 Apr 2024 *

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Title: Detection of skin issues in face using deep learning: an empirical approach for 21st century Healthcare 5.0

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