TY - JOUR N2 - Skin cancer is the most common form of cancer affecting humans. Melanoma is the most dangerous type of skin cancer; and early diagnosis is extremely vital in curing the disease. So far, the human knowledge in this field is very limited, thus, developing a mechanism capable of identifying the disease early on can save lives, reduce intervention and cut unnecessary costs. In this paper, the researchers developed a new learning technique to classify skin lesions, with the purpose of observing and identifying the presence of melanoma. This new technique is based on a convolutional neural network solution with multiple configurations; where the researchers employed an International Skin Imaging Collaboration (ISIC) dataset. Optimal results are achieved through a convolutional neural network composed of 14 layers. This proposed system can successfully and reliably predict the correct classification of dermoscopic lesions with 97.78% accuracy. L1 - http://www.czasopisma.pan.pl/Content/113310/PDF/68.pdf L2 - http://www.czasopisma.pan.pl/Content/113310 PY - 2019 IS - No 3 EP - 512 DO - 10.24425/ijet.2019.129806 KW - technology KW - dermoscopic lesions KW - convolutional neural network KW - ISIC dataset KW - deep learning KW - neural networks A1 - Mohamed, Abeer A1 - Mohamed, Wael A. A1 - Zekry, Abdel Halim PB - Polish Academy of Sciences Committee of Electronics and Telecommunications VL - vol. 65 DA - 2019.09.06 T1 - Deep Learning Can Improve Early Skin Cancer Detection SP - 507 UR - http://www.czasopisma.pan.pl/dlibra/publication/edition/113310 T2 - International Journal of Electronics and Telecommunications ER -