{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T05:20:38Z","timestamp":1773206438915,"version":"3.50.1"},"reference-count":42,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,29]],"date-time":"2022-12-29T00:00:00Z","timestamp":1672272000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002241","name":"JST","doi-asserted-by":"publisher","award":["JPMJFS2121"],"award-info":[{"award-number":["JPMJFS2121"]}],"id":[{"id":"10.13039\/501100002241","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Any change in the light-source spectrum modifies the color information of an object. The spectral distribution of the light source can be optimized to enhance specific details of the obtained images; thus, using information-enhanced images is expected to improve the image recognition performance via machine vision. However, no studies have applied light spectrum optimization to reduce the training loss in modern machine vision using deep learning. Therefore, we propose a method for optimizing the light-source spectrum to reduce the training loss using neural networks. A two-class classification of one-vs-rest among the classes, including enamel as a healthy condition and dental lesions, was performed to validate the proposed method. The proposed convolutional neural network-based model, which accepts a 5 \u00d7 5 small patch image, was compared with an alternating optimization scheme using a linear-support vector machine that optimizes classification weights and lighting weights separately. Furthermore, it was compared with the proposed neural network-based algorithm, which inputs a pixel and consists of fully connected layers. The results of the five-fold cross-validation revealed that, compared to the previous method, the proposed method improved the F1-score and was superior to the models that were using the immutable standard illuminant D65.<\/jats:p>","DOI":"10.3390\/jimaging9010007","type":"journal-article","created":{"date-parts":[[2022,12,30]],"date-time":"2022-12-30T03:31:17Z","timestamp":1672371077000},"page":"7","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["The Optimization of the Light-Source Spectrum Utilizing Neural Networks for Detecting Oral Lesions"],"prefix":"10.3390","volume":"9","author":[{"given":"Kenichi","family":"Ito","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi 441-8580, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8880-3411","authenticated-orcid":false,"given":"Hiroshi","family":"Higashi","sequence":"additional","affiliation":[{"name":"Graduate School of Informatics, Kyoto University, Yoshidahonmachi 36-1, Sakyo-ku, Kyoto 606-8501, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ari","family":"Hietanen","sequence":"additional","affiliation":[{"name":"Planmeca Oy, 00880 Helsinki, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pauli","family":"F\u00e4lt","sequence":"additional","affiliation":[{"name":"School of Computing, University of Eastern Finland, 80101 Joensuu, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kyoko","family":"Hine","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi 441-8580, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5481-0004","authenticated-orcid":false,"given":"Markku","family":"Hauta-Kasari","sequence":"additional","affiliation":[{"name":"School of Computing, University of Eastern Finland, 80101 Joensuu, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shigeki","family":"Nakauchi","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Toyohashi University of Technology, Toyohashi 441-8580, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Commission Internationale de l\u2019eclairage (1995). 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