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RETRACTION article

Front. Energy Res.
Sec. Smart Grids
Volume 13 - 2025 | doi: 10.3389/fenrg.2025.1570343
This article is part of the Research Topic Advanced Anomaly Detection Technologies and Applications in Energy Systems View all 64 articles

Retraction: Bone Age Assessment Based on Deep Convolutional Features and Fast Extreme Learning Machine Algorithm

Provisionally accepted
Frontiers Editorial Office Frontiers Editorial Office *
  • Frontiers Media SA, Lausanne, Switzerland

This article is a retraction of:

  1. Bone Age Assessment Based on Deep Convolutional Features and Fast Extreme Learning Machine Algorithm

    1. Read original article

The final, formatted version of the article will be published soon.

    Retraction: Bone Age Assessment Based on Deep Convolutional Features and Fast Extreme Learning Machine AlgorithmAuthor: Frontiers Editorial Office*, Frontiers Media SA, Lausanne, Switzerland A Retraction of the Original Research Article: Guo L, Wang J, Teng J and Chen Y (2022) Bone Age Assessment Based on Deep Convolutional Features and Fast Extreme Learning Machine Algorithm. Front. Energy Res. 9:813650. doi: 10.3389/fenrg.2021.813650The journal retracts the 19 May 2022 article cited above. “Following publication, concerns were raised regarding the scientific validity of the article. An investigation was conducted in accordance with Frontiers’ policies. The authors failed to provide raw data and/or a satisfactory explanation and as a result, the conclusions of the article have been deemed unreliable, and the article has been retracted.This retraction was approved by the Chief Executive Editor of Frontiers. The authors received a communication regarding the retraction and had a chance to respond. This communication has been recorded by the publisher. “

    Keywords: Bone age assessment, deep convolution learning, ELM, RoIs extraction, Hybrid prediction

    Received: 03 Feb 2025; Accepted: 03 Feb 2025.

    Copyright: © 2025 Editorial Office. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Frontiers Editorial Office, Frontiers Media SA, Lausanne, Switzerland

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