{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:00:43Z","timestamp":1760144443697,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T00:00:00Z","timestamp":1713830400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science KAKENHI Grant-in-Aid for Transformative Research Areas (A)","doi-asserted-by":"publisher","award":["20H05810X"],"award-info":[{"award-number":["20H05810X"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>The preparation of raw images for subsequent analysis, known as image preprocessing, is a crucial step that can boost the performance of an image classification model. Although deep learning has succeeded in image classification without handcrafted features, certain studies underscore the continued significance of image preprocessing for enhanced performance during the training process. Nonetheless, this task is often demanding and requires high-quality images to effectively train a classification model. The quality of training images, along with other factors, impacts the classification model\u2019s performance and insufficient image quality can lead to suboptimal classification performance. On the other hand, achieving high-quality training images requires effective image preprocessing techniques. In this study, we perform exploratory experiments aimed at improving a classification model of unexposed potsherd cavities images via image preprocessing pipelines. These pipelines are evaluated on two distinct image sets: a laboratory-made, experimental image set that contains archaeological images with controlled lighting and background conditions, and a J\u014dmon\u2013Yayoi image set that contains images of real-world potteries from the J\u014dmon period through the Yayoi period with varying conditions. The best accuracy performances obtained on the experimental images and the more challenging J\u014dmon\u2013Yayoi images are 90.48% and 78.13%, respectively. The comprehensive analysis and experimentation conducted in this study demonstrate a noteworthy enhancement in performance metrics compared to the established baseline benchmark.<\/jats:p>","DOI":"10.3390\/info15050243","type":"journal-article","created":{"date-parts":[[2024,4,23]],"date-time":"2024-04-23T05:28:06Z","timestamp":1713850086000},"page":"243","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving the Classification of Unexposed Potsherd Cavities by Means of Preprocessing"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8696-5687","authenticated-orcid":false,"given":"Randy Cahya","family":"Wihandika","sequence":"first","affiliation":[{"name":"Graduate School of Science and Technology, Kumamoto University, Kumamoto 860-0862, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6405-1390","authenticated-orcid":false,"given":"Yoonji","family":"Lee","sequence":"additional","affiliation":[{"name":"Graduate School of Social and Cultural Sciences, Kumamoto University, Kumamoto 860-0862, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8986-1613","authenticated-orcid":false,"given":"Mahendra","family":"Data","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science, Brawijaya University, Malang 65145, Indonesia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0861-849X","authenticated-orcid":false,"given":"Masayoshi","family":"Aritsugi","sequence":"additional","affiliation":[{"name":"Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto 860-0862, Japan"}]},{"given":"Hiroki","family":"Obata","sequence":"additional","affiliation":[{"name":"Faculty of Humanities and Social Sciences, Kumamoto University, Kumamoto 860-0862, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6819-4305","authenticated-orcid":false,"given":"Israel","family":"Mendon\u00e7a","sequence":"additional","affiliation":[{"name":"Faculty of Advanced Science and Technology, Kumamoto University, Kumamoto 860-0862, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/s10916-019-1401-7","article-title":"Age Prediction Based on Brain MRI Image: A Survey","volume":"43","author":"Sajedi","year":"2019","journal-title":"J. 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