Leveraging Machine Learning for Epidermal Ailment Detection

Title

Leveraging Machine Learning for Epidermal Ailment Detection

Creator

Saranya, R.; Sandhika, G.

Description

Skin disorders are common across the globe, often proving to be difficult to diagnose because of coexisting signs and symptoms. In this paper, we study the feasibility of using machine learning (ML) techniques for automatic skin disease detection. We look at the emerging patterns in fundamental studies within the scope of focus that deals with image processing for feature extraction and employing classification methods for disease detection. We focus on feature extraction and the classification of images. One of the major strengths is the ML-based approach with better access and usability and higher chances of them being detected at an early stage. In addition, we consider some of the drawbacks and problems of these methods, including biased data and lack of sufficient professional oversight. We also consider other aspects, whereby one of them is further analysis of the requirement in the case of the absence of the adequate data, standard models, and unambiguous explanations of the inner processes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

Source

Lecture Notes in Networks and Systems;Volume;1497 LNNS;pp.275-288

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Deep learning; Diagnosis; Image processing; Machine learning; Skin ailment detection

Coverage

Saranya R., Department of Computer Science with Data Analytics, PSG College of Arts and Science, Coimbatore, India; Sandhika G., Department of Statistics and Data Science, Christ University, Bangalore, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981969183-8;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-9184-5_22

https://www.scopus.com/pages/publications/105028358148?origin=resultslist