Melanoma Skin Cancer Classification based on CNN Deep Learning Algorithms


  • Safa Riyadh Waheed ᵃComputer Techniques Engineering Department, Faculty of Information Technology, Imam Jaafar Al-Sadiq University, Iraq; ᶜFaculty of Engineering, School of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Saadi Mohammed Saadi Ministry of Education, Iraq
  • Mohd Shafry Mohd Rahim Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Norhaida Mohd Suaib Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia
  • Fallah H Najjar Department of Computer Systems Techniques, Technical Institute of Najaf, Al-Furat Al-Awsat Technical University, Iraq
  • Myasar Mundher Adnan Islamic University, Najaf, Iraq
  • Ali Aqeel Salim Laser Center and Physics Department, Faculty of Science, Universiti Teknologi Malaysia, 81310 UTM Johor Bahru, Johor, Malaysia



Deep learning, CNN, melanoma, skin cancer, classification


Melanoma, the deadliest form of skin cancer, is on the rise. The goal of this study is to present a deep learning system implementation for the detection of melanoma lesions on a server equipped with a graphics processing unit (GPU). When applied by a dermatologist, the recommended method might aid in the early detection of this kind of skin cancer. Evidence shows that deep learning may be used in a variety of settings to successfully extract patterns from data such as signals and images. This research presents a convolution neural network–based strategy for identifying early-stage melanoma skin cancer. Images are input into a deep learning model known as a convolutional neural network (CNN) that has already been pre-trained. The CNN classifier, which is trained with large amounts of data, can discriminate between malignant and nonmalignant melanoma. The method's success in the lab bodes well for its potential to aid dermatologists in the early detection of melanoma. However, the experimental results show that the proposed technique excels beyond the state-of-the-art methods in terms of diagnostic accuracy.


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