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<p>Medical picture categorization has been greatly enhanced by the use of deep learning, especially in the timely identification of skin lesions. Still, predictions from a single model remain unreliable due to their susceptibility to variations in the dataset, complicating their application to diverse clinical scenarios. This study introduces a unique CNN, PHMBCNN, designed to enhance classification accuracy using a progressive learning strategy. We propose the R3MV three-tier decision fusion system, which integrates predictions from (i) individual CNN model predictions, (ii) a feature fusion classification architecture, and (iii) a meta-classifier trained on the outputs of the CNN models. The final forecast is reached using a majority voting procedure, which enhances the reliability of the decision. The study utilizes two datasets for skin cancer: PAD_UFES_20 and HAM10000. Incorporating a GRU into the PHMBCNN model results in the PHMBCNN-GRU. The classification accuracy enhanced from 75.70% for the PAD_UFES_20 dataset to 80.69%, and from 92.07% for the HAM10000 dataset to 96.01%. The R3MV system design achieves 81.78% for PAD_UFES_20 and 99.3% for HAM10000.</p>

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R3MV: a novel reliable system architecture for skin cancer classification using progressive heterogeneous multiblock model

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月16日

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  • S. K. Reshma,
  • S. R. Reeja

摘要

Medical picture categorization has been greatly enhanced by the use of deep learning, especially in the timely identification of skin lesions. Still, predictions from a single model remain unreliable due to their susceptibility to variations in the dataset, complicating their application to diverse clinical scenarios. This study introduces a unique CNN, PHMBCNN, designed to enhance classification accuracy using a progressive learning strategy. We propose the R3MV three-tier decision fusion system, which integrates predictions from (i) individual CNN model predictions, (ii) a feature fusion classification architecture, and (iii) a meta-classifier trained on the outputs of the CNN models. The final forecast is reached using a majority voting procedure, which enhances the reliability of the decision. The study utilizes two datasets for skin cancer: PAD_UFES_20 and HAM10000. Incorporating a GRU into the PHMBCNN model results in the PHMBCNN-GRU. The classification accuracy enhanced from 75.70% for the PAD_UFES_20 dataset to 80.69%, and from 92.07% for the HAM10000 dataset to 96.01%. The R3MV system design achieves 81.78% for PAD_UFES_20 and 99.3% for HAM10000.