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March 29, 2026Cluster Computing0 citationsOpen Access

Correction: An effective multiclass skin cancer classification approach based on deep convolutional neural network

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EHEssam H. HousseinUniversitas 17 Agustus 1945 SemarangDADoaa A. AbdelkareemLux Research (United States)GHGang HuXi'an University of Technology

Key Points

  • The aim is to evaluate the performance of a deep convolutional neural network in classifying skin cancer.
  • Utilized a deep convolutional neural network for classification
  • Evaluated performance using accuracy, recall, precision, F1-score, specificity, and AUC
  • Corrected a typographical error in the performance metrics section
  • The evaluation showcased the DCNN's effectiveness in accurately classifying skin cancer
  • Performance metrics provided a comprehensive understanding of the model's capabilities

Abstract

1 0 0 7 / s 1 0 5 8 6 -0 2 4 -0 4 5 4 0 -1In this article under the section heading '5.1 Performance metrics' the word 'six' has been incorrectly written as 'sex' in the following sentence: "The performance of the proposed DCNN model was evaluated using sex evaluation metrics including accuracy, recall, precision, F1-score, specificity, and AUC.The incorporation of these metrics provided a nuanced under standing of the DCNN's performance across different facets, ensuring a comprehensive evaluation of its effectiveness in skin cancer classification''.

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Cite This Study

Houssein et al. (2026) studied this question.

synapsesocial.com/papers/69c8c195de0f0f753b39bf1ehttps://doi.org/10.1007/s10586-026-06099-5
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