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Skin Cancer Malignancy Classification and Segmentation Using Machine Learning Algorithms

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Abstract

Malignant melanoma is the deadliest form of skin cancer. Once it metastasizes from its origin into other tissues, there is no surgical removal option as a treatment. The only way to improve the cure rate is through early diagnosis. Many machine learning (ML) algorithms have been proposed for early skin cancer classification and segmentation. Each algorithm performs well in certain situations; therefore, the selection of ML algorithm is the key to better accuracy. This article surveys many research studies that used ML algorithms (i.e., supervised and unsupervised skin cancer malignancy classification and segmentation). We present some objectives and limitations of the surveyed papers. We also provide future directions for better skin cancer malignancy detection with high accuracy.

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Authors and Affiliations

  1. Department of Computer Science, University of Engineering and Technology, Taxila, 47080, Pakistan

    Noor ul Huda, Rashid Amin & Syeda Irum Gillani

  2. Department of Computer Science, University of Chakwal, Chakwal, 48800, Pakistan

    Rashid Amin

  3. Knowledge Unit of Systems and Technology, University of Management and Technology, Sialkot, 51040, Pakistan

    Mudassar Hussain

  4. Department of Information Technology, The University of Haripur, Haripur, 22780, KPK, Pakistan

    Adeel Ahmed

  5. Prince Abdullah Bin Ghazi Faculty of Information and Communication Technology, Al-Balqa Applied University, Al-Salt, 19385, Jordan

    Hamza Aldabbas

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  1. Noor ul Huda
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Correspondence to Rashid Amin.

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ul Huda, N., Amin, R., Gillani, S.I. et al. Skin Cancer Malignancy Classification and Segmentation Using Machine Learning Algorithms. JOM 75, 3121–3135 (2023). https://doi.org/10.1007/s11837-023-05856-w

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