I+NIR-SC-UFES: a multimodal image and Near-Infrared (NIR) spectroscopy dataset for skin cancer

Published: 14 August 2026| Version 1 | DOI: 10.17632/fttnv8x2t5.1
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Description

Dataset Summary The collection of the Image & NIR Spectroscopy for Skin Cancer (I+NIR-SC-UFES) dataset took about one year and resulted in a multimodal dataset containing 774 samples with consistent image–spectrum pairs. The images were acquired using a smartphone under natural light, and the spectra were collected using near-infrared (NIR) spectroscopy. Data acquisition was conducted in partnership with the Dermatological and Surgical Assistance Program (PAD, in Portuguese: Programa de Assistência Dermatológica e Cirúrgica), a nonprofit program at the Federal University of Espírito Santo (UFES, Brazil). The PAD program focuses on providing free treatment for skin lesions, particularly benefiting low-income individuals in rural areas. The dataset encompasses images of six distinct skin lesion categories: - Basal Cell Carcinoma (BCC), Squamous Cell Carcinoma (SCC), and Melanoma (MEL), corresponding to biopsy-confirmed skin cancers; - Actinic Keratosis (ACK), Seborrheic Keratosis (SEK), and Nevus (NEV), corresponding to non-cancerous skin lesions. The .csv file contains 125 spectral values for each sample, with each value representing the average measurement over a 6.4 nm interval across the wavelength range from 900 to 1700 nm. The file also includes the identifier of the corresponding image, which is stored in the imgs directory. All images are provided in .jpg format. Ethics statement The dataset was collected along with the Dermatological and Surgical Assistance Program (PAD) of the Federal University of Espírito Santo. The program is managed by the Department of Specialized Medicine and was approved by the university ethics committee (nº 500002/478) and the Brazilian government through Plataforma Brasil (nº 4.007.097), the Brazilian agency responsible for research involving human beings. In addition, all data is collected under patient consent and the patient’s privacy is completely preserved.

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Artificial Intelligence, Skin Cancer, Near Infrared Spectroscopy, Cancer Research, Clinical Imaging

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