Use of hyperspectral anomaly detection in Dermatology

Authors

https://doi.org/10.22105/thi.v3i2.54

Abstract

Hyperspectral anomaly detection in dermatology refers to a technique that uses Hyperspectral Imaging (HSI) to identify subtle abnormalities in skin tissue. Unlike traditional imaging, HSI captures data across hundreds of narrow spectral bands to provide rich spectral information beyond the visible range. This makes it a powerful tool for detecting early-stage skin conditions like inflammation, precancerous lesions, or subtle malignancies to improve early diagnosis and precision in dermatology. In this paper, we investigate hyperspectral anomaly detection methods in dermatology, synthesizing key advancements from past studies and highlighting future potentials. By integrating HSI with machine learning, researchers have consistently demonstrated enhanced sensitivity and specificity in distinguishing malignant from benign skin lesions. Early efforts, such as spectral band analysis and simple classifiers, laid the groundwork, while recent approaches leverage deep learning and AI for real-time, automated anomaly detection. As the field advances, despite benefits, serious potential challenges are emerged such as diverse datasets, standardization, and clinical integration. Ultimately, this paper envisions hyperspectral anomaly detection as a transformative tool for non-invasive, precise dermatologic diagnosis in the near future.

Keywords:

Anomaly detection, Biomedical image processing, Hyperspectral imaging, Hyperspectral image processing, Dermatology

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Published

2026-06-08

Issue

Vol. 3 No. 2 (2026): Trends in Health Informatics

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Articles

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How to Cite

Vafadar, M., & Erdinc Kocer, H. . (2026). Use of hyperspectral anomaly detection in Dermatology. Trends in Health Informatics, 3(2), 88-97. https://doi.org/10.22105/thi.v3i2.54

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