Corrections to 'Deep-Learning Approach for Tissue Classification Using Acoustic Waves During Ablation With an Er:YAG Laser'
| Autor: | Carlo Seppi, Antal Huck, Herve Nguendon Kenhagho, Eva Schnider, Georg Rauter, Azhar Zam, Philippe C. Cattin |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | IEEE Access, Vol 12, Pp 69299-69300 (2024) |
| Druh dokumentu: | article |
| ISSN: | 2169-3536 |
| DOI: | 10.1109/ACCESS.2024.3395071 |
| Popis: | In the above article [1], we found major issues with the data we used. Specifically, we received data from [2] for five distinct tissues, with ten specimens per tissue. However, upon closer examination, we realized that the data for these specimens were not unique; rather, they were scaled variations derived from a single specimen. As a result, our training, testing, and validation datasets were not independent, leading to an artificially high accuracy rate of 100%. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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