Melanoma Detection Using a Deep Learning Approach


  • Sohail Manzoor University of Engineering and technology Taxila
  • Huma Qayyum University of Engineering and Technology Taxila
  • Farman Hassan University of engineering and teachnology taxila
  • Asad Ullah University of Engineering and Technology Taxila
  • Ali Nawaz University of Engineering and Technology Taxila
  • Auliya Ur Rahman University of Engineering and Technology Taxila


Melanoma Identification, Melanoma detection, Deep Learning, VGG16, ResNet50, Data Augmentation.


Melanoma is a skin lesion disease; it is a skin cancer that is caused by uncontrolled growth in melanocytic tissues. Damaged cells can cause damage to nearby cells and consequently spreads cancer in other parts of the body. The aim of this research is the early detection of Melanoma disease, many researchers have already struggled and achieved success in detecting melanoma with different values for their evaluation parameters, they used different machine learning as well as deep learning approaches, and we applied deep learning approach for Melanoma detection, we used publicly available dataset for experimentation purpose. We applied deep learning algorithms ResNet50 and VGG16 for Melanoma detection; the accuracy, precision, recall, Jaccard index, and dice co-efficient of our proposed model are 92.3%, 93.3%, 90%, 9.98%, and 97.7%, respectively. Our proposed algorithm can be used to increase chances of survival for patients and can save the money which is used for diagnosis and treatment of Melanoma every year.

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How to Cite

Sohail Manzoor, Huma Qayyum, Farman Hassan, Asad Ullah, Ali Nawaz, & Auliya Ur Rahman. (2022). Melanoma Detection Using a Deep Learning Approach. International Journal of Innovations in Science & Technology, 4(1), 222–232. Retrieved from