Binary Classification of Skin Cancer Images Using Pre-trained Networks with I-GWO


  • Hadeer Hussein Suez Canal University, Ismailia, Egypt.
  • Ahmed Magdy Suez Canal University, Ismailia, Egypt.
  • Rehab F. Abdel-Kader Port said University, Port said, Egypt
  • Khaled Abd El Salam Misr University for Science and Technology, Egypt.



Binary classification, Deep learning, Dermoscopic images, I-GWO, Pretrained networks, Skin cancer


One of the most prevalent forms of cancer worldwide is skin cancer. Determining disease characteristics necessitates a clinical evaluation of skin lesions, but this process is limited by long time horizons and a multiplicity of interpretations. Deep learning techniques have been created to help dermatologists with these issues as a higher patient survival rate depends on the early and precise detection of skin cancer. This research proposed a new approach for binary classification of dermoscopic images for skin cancer. The Improved Grey Wolf Optimizer (I-GWO) is used in this technique to fine-tune some hyperparameters’ values of various pre-trained deep learning networks to maximize results. SqueezeNet, ShuffleNet, AlexNet, ResNet-18, and DarkNet-19 are the pre-trained networks that were employed. We tested the MED-NODE and DermIS databases in our investigation. Concerning the MED-NODE and DermIS datasets, the proposed method's highest accuracy results are 100% and 97%, respectively.


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How to Cite

Hussein, H., Ahmed Magdy, Abdel-Kader , R. F., & Khaled Abd El Salam. (2024). Binary Classification of Skin Cancer Images Using Pre-trained Networks with I-GWO. Inteligencia Artificial, 27(74), 102–116.