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<p>The accurate and early classification of skin lesions remains a critical concern in dermatological diagnostics due to the visual similarity between benign and malignant conditions. This study proposes an enhanced skin lesion classification framework that combines K-Means image segmentation, transfer learning, and the lightweight MobileNet architecture. The HAM10000 dataset was employed, and preprocessing involved augmentation, normalization, and class balancing to improve model generalization. K-Means clustering was utilized to segment the lesion area, improving the model’s focus on the region of interest. Transfer learning was applied using a pre-trained MobileNet model, which was fine-tuned on the dataset to enable efficient and accurate classification. The model was evaluated across multiple training epochs, with the best results achieved at epoch 50: 98.20% accuracy, 93.75% precision, 100% recall, and 97.10% F1-score, alongside an AUC of 98.00%. Comparative analysis demonstrates that the proposed model outperforms existing deep learning models in both diagnostic accuracy and computational efficiency. These results indicate that the integration of K-Means segmentation with MobileNet and transfer learning provides a robust solution for real-time and resource-efficient skin lesion classification in clinical applications.</p>

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Improving skin lesion classification using k-means, transfer learning and mobilenet architecture

  • 期刊论文

  • 发表日期：

    2025年7月21日

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  • Akinbowale Nathaniel Babatunde,
  • Bukola Fatimah Balogun,
  • Ayodele Joseph Babatunde,
  • Salman Olatunji Isiaka,
  • Joseph Bamidele Awotunde,
  • Abidemi Emmanuel Adeniyi

摘要

The accurate and early classification of skin lesions remains a critical concern in dermatological diagnostics due to the visual similarity between benign and malignant conditions. This study proposes an enhanced skin lesion classification framework that combines K-Means image segmentation, transfer learning, and the lightweight MobileNet architecture. The HAM10000 dataset was employed, and preprocessing involved augmentation, normalization, and class balancing to improve model generalization. K-Means clustering was utilized to segment the lesion area, improving the model’s focus on the region of interest. Transfer learning was applied using a pre-trained MobileNet model, which was fine-tuned on the dataset to enable efficient and accurate classification. The model was evaluated across multiple training epochs, with the best results achieved at epoch 50: 98.20% accuracy, 93.75% precision, 100% recall, and 97.10% F1-score, alongside an AUC of 98.00%. Comparative analysis demonstrates that the proposed model outperforms existing deep learning models in both diagnostic accuracy and computational efficiency. These results indicate that the integration of K-Means segmentation with MobileNet and transfer learning provides a robust solution for real-time and resource-efficient skin lesion classification in clinical applications.