IEEE Access (Jan 2024)

Corrections to “Deep-Learning Approach for Tissue Classification Using Acoustic Waves During Ablation With an Er:YAG Laser”

  • Carlo Seppi,
  • Antal Huck,
  • Herve Nguendon Kenhagho,
  • Eva Schnider,
  • Georg Rauter,
  • Azhar Zam,
  • Philippe C. Cattin

DOI
https://doi.org/10.1109/ACCESS.2024.3395071
Journal volume & issue
Vol. 12
pp. 69299 – 69300

Abstract

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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%.