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Skin Cancer Detection: A Review Using Deep Learning Techniques

Author

Listed:
  • Mehwish Dildar

    (Government Associate College for Women Mari Sargodha, Sargodha 40100, Pakistan)

  • Shumaila Akram

    (Department of Computer Science and Information Technology, University of Sargodha, Sargodha 40100, Pakistan)

  • Muhammad Irfan

    (Electrical Engineering Department, College of Engineering, Najran University Saudi Arabia, Najran 61441, Saudi Arabia)

  • Hikmat Ullah Khan

    (Department of Computer Science, Wah Campus, Comsats University, Wah Cantt 47040, Pakistan)

  • Muhammad Ramzan

    (Department of Computer Science and Information Technology, University of Sargodha, Sargodha 40100, Pakistan
    Department of Computer Science, School of Systems and Technology, University of Management and Technology, Lahore 54782, Pakistan)

  • Abdur Rehman Mahmood

    (Department of Computer Science, COMSATS University Islamabad, Islamabad 440000, Pakistan)

  • Soliman Ayed Alsaiari

    (Department of Internal Medicine, Faculty of Medicine, Najran University, Najran 61441, Saudi Arabia)

  • Abdul Hakeem M Saeed

    (Department of Dermatology, Najran University Hospital, Najran 61441, Saudi Arabia)

  • Mohammed Olaythah Alraddadi

    (Department of Internal Medicine, Faculty of Medicine, University of Tabuk, Tabuk 71491, Saudi Arabia)

  • Mater Hussen Mahnashi

    (Department of Medicinal Chemistry, Pharmacy School, Najran University, Najran 61441, Saudi Arabia)

Abstract

Skin cancer is one of the most dangerous forms of cancer. Skin cancer is caused by un-repaired deoxyribonucleic acid (DNA) in skin cells, which generate genetic defects or mutations on the skin. Skin cancer tends to gradually spread over other body parts, so it is more curable in initial stages, which is why it is best detected at early stages. The increasing rate of skin cancer cases, high mortality rate, and expensive medical treatment require that its symptoms be diagnosed early. Considering the seriousness of these issues, researchers have developed various early detection techniques for skin cancer. Lesion parameters such as symmetry, color, size, shape, etc. are used to detect skin cancer and to distinguish benign skin cancer from melanoma. This paper presents a detailed systematic review of deep learning techniques for the early detection of skin cancer. Research papers published in well-reputed journals, relevant to the topic of skin cancer diagnosis, were analyzed. Research findings are presented in tools, graphs, tables, techniques, and frameworks for better understanding.

Suggested Citation

  • Mehwish Dildar & Shumaila Akram & Muhammad Irfan & Hikmat Ullah Khan & Muhammad Ramzan & Abdur Rehman Mahmood & Soliman Ayed Alsaiari & Abdul Hakeem M Saeed & Mohammed Olaythah Alraddadi & Mater Husse, 2021. "Skin Cancer Detection: A Review Using Deep Learning Techniques," IJERPH, MDPI, vol. 18(10), pages 1-22, May.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5479-:d:558627
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    Citations

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    Cited by:

    1. Huan Wu & Shuiping Cheng & Kunlun Xin & Nian Ma & Jie Chen & Liang Tao & Min Gao, 2022. "Water Quality Prediction Based on Multi-Task Learning," IJERPH, MDPI, vol. 19(15), pages 1-19, August.
    2. Ali Raza & Akhtar Ali & Sami Ullah & Yasir Nadeem Anjum & Basit Rehman, 2025. "Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems," PLOS ONE, Public Library of Science, vol. 20(3), pages 1-37, March.
    3. Mahnoor Sardar & Muhammad Majid Niazi & Fawad Nasim, 2024. "Ensemble Deep Learning Methods for Detecting Skin Cancer," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(1), pages 673-682.
    4. Saleem Mustafa & Arfan Jaffar & Muhammad Rashid & Sheeraz Akram & Sohail Masood Bhatti, 2025. "Deep learning-based skin lesion analysis using hybrid ResUNet++ and modified AlexNet-Random Forest for enhanced segmentation and classification," PLOS ONE, Public Library of Science, vol. 20(1), pages 1-20, January.
    5. Ahmad Naeem & Tayyaba Anees & Mudassir Khalil & Kiran Zahra & Rizwan Ali Naqvi & Seung-Won Lee, 2024. "SNC_Net: Skin Cancer Detection by Integrating Handcrafted and Deep Learning-Based Features Using Dermoscopy Images," Mathematics, MDPI, vol. 12(7), pages 1-35, March.
    6. Muhammad Amir khan & Muhammad Danish Ali & Tehseen Mazhar & Tariq Shahzad & Waheed Ur Rehman & Mohammad Shahid & Habib Hamam, 2025. "An Advanced Deep Learning Framework for Skin Cancer Classification," The Review of Socionetwork Strategies, Springer, vol. 19(1), pages 111-130, April.

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