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Enhanced Skin Cancer Classification with MobileNetV3 and Morphological Preprocessing: A Deep Learning-Based Extension

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Listed:
  • Sidra Mehboob, Maryam Bukhari, Yaser Ali Shah, SalabatKhan, MuhammadSharif

    (Department of Computer Science COMSATS University Islamabad, Attock Campus Attock 43600, Pakistan. Big Data Research Center Jeju National UniversityJeju 63243, Republic of Korea)

Abstract

Skin cancer detection continues to pose challenges due to the visual similarity between the types of lesions and the limitations of traditional diagnostic methods. This study presents an extended and improved skin lesion classification framework that combines transfer learning with MobileNetV3 and enhanced preprocessing using mathematical morphological techniques. These preprocessing methods refine lesion boundaries and suppress irrelevant structures in dermoscopic images, thereby improving feature discrimination during training. The refined framework is evaluated using the ISIC dataset and achieves a notable classification accuracy of 89%, showing superior performance compared to baseline models. This extension also examines the generalizability and suitability of the model for deployment in low-resource mobile settings. The results validate the effectiveness of lightweight architectures paired with morphological enhancements, providing a reliable and scalable solution for early skin cancer screening and clinical support.

Suggested Citation

  • Sidra Mehboob, Maryam Bukhari, Yaser Ali Shah, SalabatKhan, MuhammadSharif, 2025. "Enhanced Skin Cancer Classification with MobileNetV3 and Morphological Preprocessing: A Deep Learning-Based Extension," International Journal of Innovations in Science & Technology, 50sea, vol. 7(7), pages 1-12, May.
    • Handle: RePEc:abq:ijist1:v:7:y:2025:i:7:p:1-12
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    References listed on IDEAS

    as
    1. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Correction: Corrigendum: Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 546(7660), pages 686-686, June.
    2. Andre Esteva & Brett Kuprel & Roberto A. Novoa & Justin Ko & Susan M. Swetter & Helen M. Blau & Sebastian Thrun, 2017. "Dermatologist-level classification of skin cancer with deep neural networks," Nature, Nature, vol. 542(7639), pages 115-118, February.
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